Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
Home Page: https://arxiv.org/pdf/1706.02275.pdf
License: MIT License
Hello,
I thank you OpenAI, amazing contributions, the papers and codes help my research work a lot.
I wonder how come the rewards for adversaries in simple_tag.py are same among them while checking MADDPG working on multiagent-particle-envs. As far as I looked through, I guess it is a bug (not sure it is not supposed) at the reward function adversary_reward() in simple_tag.py (called from reward() <- _get_reward() of MultiAgentEnv in environment.py <- step() <- env.step()... for example).
The bug is, each adversarial agent gets its own reward from other adversarial agents' reward, so every time their rewards are same, because do they share their reward together..? Please check the code below if need to fix.
The mentioned function in simple_tag.py is:
def adversary_reward(self, agent, world):
# Adversaries are rewarded for collisions with agents
rew = 0
shape = False
agents = self.good_agents(world)
adversaries = self.adversaries(world)
if shape: # reward can optionally be shaped (decreased reward for increased distance from agents)
for adv in adversaries:
rew -= 0.1 * min([np.sqrt(np.sum(np.square(a.state.p_pos - adv.state.p_pos))) for a in agents])
if agent.collide:
for ag in agents:
for adv in adversaries:
if self.is_collision(ag, adv):
rew += 10
return rew
then the last part:
if agent.collide:
for ag in agents:
for adv in adversaries:
if self.is_collision(ag, adv):
rew += 10
return rew
should be:
if agent.collide:
for ag in agents:
if self.is_collision(ag, adv):
rew += 10
return rew
With this fix, each agent, with MADDPG in simple_tag.py, gets each reward.
Thanks.
Hi, I was wondering whether there's a feature to have a single, fixed frame rendering the entire environment (as shown in your demo video in this blog post), rather that individual windows rendering each agent's view for the interactive mode.
Thanks!
Hi,
Is there an example somewhere that shows how to train using the baselines implementations for DDPG or other system? Could you give a few pointers on what I'd have to do to try the baselines algorithm implementations on your environment?
I was wondering also environment did you originally train in?
Thanks in advance.
Hi, I have an uncertainty thing about the paper. If I have 5 agents in the environment, I need to build 10 networks(5 for actors, 5 for critics) based on the algorithm from the paper, right?
Hi, when I tried to run the interative.py, I came across some errors:
"
Traceback (most recent call last):
File "interactive.py", line 21, in
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, info_callback=None, shared_viewer = False)
File "/home/shao/projects-py2/rl/multiagent-particle-envs/bin/../multiagent/environment.py", line 68, in init
self.observation_space.append(spaces.Box(low=-np.inf, high=+np.inf, shape=(obs_dim,), dtype=np.float32))
TypeError: init() got an unexpected keyword argument 'dtype'
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/usr/lib/python2.7/atexit.py", line 24, in _run_exitfuncs
func(*targs, **kargs)
File "/home/shao/projects-py2/gym/gym/utils/closer.py", line 67, in close
closeable.close()
File "/home/shao/projects-py2/gym/gym/core.py", line 164, in close
self.render(close=True)
TypeError: render() got an unexpected keyword argument 'close'
"
I tried to reinstall the gym, but it also failed.
I would appreciate it if anyone could help me.
As in the title, seems the velocity of each agent is not coded in
Hello, I am getting the following error when trying to display a trained policy in simple_crypto
The command I am running:
$ python experiments/train.py --scenario=simple_crypto --display
The trace:
Traceback (most recent call last):
File "experiments/train.py", line 202, in
train(arglist)
File "experiments/train.py", line 153, in train
env.render()
File "/home/ryan/openai/multiagent-particle-envs/multiagent/environment.py", line 233, in render
geom.set_color(*entity.color, alpha=0.5)
TypeError: set_color() got multiple values for argument 'alpha'
Hi, problem seems to be related to version of gym used for multiagent-particle-envs.
Every time when I'm using methods of env like reset or render I'm getting this error.
states = env.reset()
File "/home/yakotaki/Downloads/gym-master/gym/core.py", line 71, in reset
raise NotImplementedError
NotImplementedError
after downgrading gym to any ver < 0.10 error disappear
Can you please add a friendly license to the code?
Seems like all landmarks in scenarios are unmovable. Wondering if someone can provide a case when they are movable?
Traceback (most recent call last):
File "C:/Users/gajam/multiagent-particle-envs/bin/interactive.py", line 36, in
env.render()
File "C:\Users\gajam\multiagent-particle-envs\bin..\multiagent\environment.py", line 261, in render
results.append(self.viewers[i].render(return_rgb_array = mode=='rgb_array'))
File "C:\Users\gajam\multiagent-particle-envs\bin..\multiagent\rendering.py", line 110, in render
self.window.flip()
File "C:\Users\gajam\Anaconda3\lib\site-packages\pyglet\window\win32_init_.py", line 309, in flip
self.context.flip()
AttributeError: 'NoneType' object has no attribute 'flip'
I changed the gym version. but still, have this problem. anybody can tell me what should do change?
Hi everybody,
I run into a weird behaviour in the environment.py file in line: 56.
# total action space
if len(total_action_space) > 1:
# all action spaces are discrete, so simplify to MultiDiscrete action space
if all([isinstance(act_space, spaces.Discrete) for act_space in total_action_space]):
act_space = spaces.MultiDiscrete([[0,act_space.n-1] for act_space in total_action_space])
creates an error in the mulidiscrete space. It says that nvec expects an 1 dimensional vector, but it recieves a 2 dimensional vector.
Hi, as the tilte shows, would you please shown us an example of how to run the basic experiment to help understand the algorithm ?
I wonder whether the framework support agents' partial observation.for example ,every agent just can see 1 miles around itself.
Should done function return true only when agent is out of range?
def done(self, agent, world):
x = abs(agent.state.p_pos[0])
y = abs(agent.state.p_pos[1])
if (x > 1.0 or y > 1.0):
return True
return False
Or I also should add condition for terminating episode when goal is reached?
Thanks and sorry, I'm newbie here.
Is there a way to receive the entire rendering screen as observation of each robot without actually rendering anything on the screen?
Is anyone else having trouble closing the environment after it has been rendered? After I run the following code, the window just hangs there.
env.render()
** stuff
env.close()
The reward for the simple_reference scenario is defined as follows:
def reward(self, agent, world):
if agent.goal_a is None or agent.goal_b is None:
return 0.0
dist2 = np.sum(np.square(agent.goal_a.state.p_pos - agent.goal_b.state.p_pos))
return -dist2So every agent is rewarded based on the performance of the other agent, but shouldn't be the reward be the sum of both agents' rewards? For example, something like:
def reward(self, agent, world):
reward = 0.0
for agent in world.agents:
if agent.goal_a is None or agent.goal_b is None:
continue
reward -= np.sum(np.square(agent.goal_a.state.p_pos - agent.goal_b.state.p_pos))
return rewardIn the first case, the agents have no direct incentive to move towards the goal because they only get rewarded if the other agent is close to their goal, but in the second case every agent is incentivised to communicate the goal to the other agent and to move toward its own goal.
So wouldn't be a shared reward more appropriate or is this a desired challenge which algorithms have to overcome?
Hello Developers,
My Linux Ubuntu 18.04 x64 environment:
uname -a
Linux WorkStation 5.0.0-32-generic #34~18.04.2-Ubuntu SMP Thu Oct 10 10:36:02 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
python3 --version
Python 3.6.8
pip3 list
Package Version Location
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baselines 0.1.5 /home/dragon/quant/python/baselines
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googleapis-common-protos 1.6.0
gpg 1.10.0
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grpc-google-iam-v1 0.11.4
grpcio 1.20.1
gym 0.10.5
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gym-test 0.0.1 /home/dragon/reinforcementlearning/gym-test
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I executed the python script:
~/machine_learning/multiagent-particle-envs/multiagent/scenarios$ python3 ../../bin/interactive.py --scenario simple_world_comm.py
It ran and generate few windows with circles on it. Soon after that, it hang or the circles not moving around or interacting with one and another.
Some of the end result/log:
First attempt:
~/machine_learning/multiagent-particle-envs/multiagent/scenarios$ python3 ../../bin/interactive.py --scenario simple_world_comm.py
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
Traceback (most recent call last):
File "../../bin/interactive.py", line 36, in <module>
env.render()
File "/home/dragon/machine_learning/multiagent-particle-envs/bin/../multiagent/environment.py", line 261, in render
results.append(self.viewers[i].render(return_rgb_array = mode=='rgb_array'))
File "/home/dragon/machine_learning/multiagent-particle-envs/bin/../multiagent/rendering.py", line 104, in render
self.window.dispatch_events()
File "/home/dragon/.local/lib/python3.6/site-packages/pyglet/window/xlib/__init__.py", line 856, in dispatch_events
0x1ffffff, byref(e)):
ctypes.ArgumentError: argument 2: <class 'TypeError'>: wrong type
Second attempt:
~/machine_learning/multiagent-particle-envs/multiagent/scenarios$ python3 ../../bin/interactive.py --scenario simple_world_comm.py
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
agent 1 to agent 0: _ agent 2 to agent 0: _ agent 3 to agent 0: _ agent 4 to agent 0: _ agent 5 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 3 to agent 1: _ agent 4 to agent 1: _ agent 5 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _ agent 3 to agent 2: _ agent 4 to agent 2: _ agent 5 to agent 2: _ agent 0 to agent 3: _ agent 1 to agent 3: _ agent 2 to agent 3: _ agent 4 to agent 3: _ agent 5 to agent 3: _ agent 0 to agent 4: _ agent 1 to agent 4: _ agent 2 to agent 4: _ agent 3 to agent 4: _ agent 5 to agent 4: _ agent 0 to agent 5: _ agent 1 to agent 5: _ agent 2 to agent 5: _ agent 3 to agent 5: _ agent 4 to agent 5: _
^CTraceback (most recent call last):
File "../../bin/interactive.py", line 36, in <module>
env.render()
File "/home/dragon/machine_learning/multiagent-particle-envs/bin/../multiagent/environment.py", line 261, in render
results.append(self.viewers[i].render(return_rgb_array = mode=='rgb_array'))
File "/home/dragon/machine_learning/multiagent-particle-envs/bin/../multiagent/rendering.py", line 124, in render
self.window.flip()
File "/home/dragon/.local/lib/python3.6/site-packages/pyglet/window/xlib/__init__.py", line 500, in flip
self.context.flip()
File "/home/dragon/.local/lib/python3.6/site-packages/pyglet/gl/xlib.py", line 360, in flip
self._wait_vsync()
File "/home/dragon/.local/lib/python3.6/site-packages/pyglet/gl/xlib.py", line 245, in _wait_vsync
2, (count.value + 1) % 2, byref(count))
KeyboardInterrupt
I hope the developer or someone from the community able to resolve or figure what wrong with the mentioned issue.
Thank you
reward is an undefined name in this context. Should it be self.reward instead?
flake8 testing of https://github.com/openai/multiagent-particle-envs on Python 3.6.3
$flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics
./multiagent/scenarios/simple_speaker_listener.py:60:16: F821 undefined name 'reward'
return reward(agent, reward)
^
./multiagent/scenarios/simple_speaker_listener.py:60:30: F821 undefined name 'reward'
return reward(agent, reward)
^
2 F821 undefined name 'reward'
2
The problem of prng is that the gym package is updating, and prng function has been deleted.
you can fix this in code multiagent-particle-envs/multiagent/multi_discrete.py
import gym
# from gym.spaces import prng # this prng has been canceledand change this line with:
np_random = np.random.RandomState()
random_array = np_random.rand(self.num_discrete_space)agent 1 to agent 0: _ agent 2 to agent 0: _ agent 0 to agent 1: _ agent 2 to agent 1: _ agent 0 to agent 2: _ agent 1 to agent 2: _
Traceback (most recent call last):
File "./interactive.py", line 34, in <module>
obs_n, reward_n, done_n, _ = env.step(act_n)
File "/mnt//Programming/python/multiagent-particle-envs/bin/../multiagent/environment.py", line 94, in step
reward_n.append(self._get_reward(agent))
File "/mnt//Programming/python/multiagent-particle-envs/bin/../multiagent/environment.py", line 141, in _get_reward
return self.reward_callback(agent, self.world)
File "/mnt//Programming/python/multiagent-particle-envs/bin/../multiagent/scenarios/simple_crypto.py", line 96, in reward
return self.adversary_reward(agent, world) if agent.adversary else self.agent_reward(agent, world)
File "/mnt//Programming/python/multiagent-particle-envs/bin/../multiagent/scenarios/simple_crypto.py", line 120, in adversary_reward
if not (agent.state.c == np.zeros(world.dim_c)).all():
AttributeError: 'bool' object has no attribute 'all'
$ pip list | grep "gym|numpy"
gym 0.10.5
numpy 1.14.5
numpy-stl 2.8.0
$ python -V
Python 3.6.0
(a.state.c == np.zeros(world.dim_c)).all()
Can anyone tell me what this means?
Thanks
Traceback (most recent call last):
File "D:/My_Research/MAS_code/Python/multiagent-particle-envs-master/bin/interactive.py", line 36, in
env.render()
File "D:\My_Research\MAS_code\Python\multiagent-particle-envs-master\bin..\multiagent\environment.py", line 261, in render
results.append(self.viewers[i].render(return_rgb_array = mode=='rgb_array'))
File "D:\My_Research\MAS_code\Python\multiagent-particle-envs-master\bin..\multiagent\rendering.py", line 110, in render
self.window.flip()
File "C:\Python27\lib\site-packages\pyglet\window\win32_init_.py", line 323, in flip
self.context.flip()
AttributeError: 'NoneType' object has no attribute 'flip'
Process finished with exit code 1
Did anyone fix this issue?
I am referring to the following function:
def is_collision(self, agent1, agent2):
delta_pos = agent1.state.p_pos - agent2.state.p_pos
dist = np.sqrt(np.sum(np.square(delta_pos)))
dist_min = agent1.size + agent2.size
return True if dist < dist_min else False
As I understand it, the above code is supposed to detect when an agent collides with another agent. However, it currently returns false positives when an agent "collides with itself". This is because the calling function passes the same agent entity in for agent1 and agent2 periodically within its loop.
In my fork, I have included the following lines of code to fix the bug:
if agent1 == agent2:
return False
So the code now reads:
def is_collision(self, agent1, agent2):
if agent1 == agent2:
return False
delta_pos = agent1.state.p_pos - agent2.state.p_pos
dist = np.sqrt(np.sum(np.square(delta_pos)))
dist_min = agent1.size + agent2.size
return True if dist < dist_min else False
Please let me know if I have misunderstood something. Thanks :)
ipdb import command doesn't work in environment although I completed setup successfully, I needed to install it manually via pip.
Hi.
I just cloned the repository and tried to run interactive.py, but got the following traceback:
WARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.
Traceback (most recent call last):
File "bin/interactive.py", line 21, in
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, info_callback=None, shared_viewer = False)
File "/home/name/code/multiagent-particle-envs/bin/../multiagent/environment.py", line 68, in init
self.observation_space.append(spaces.Box(low=-np.inf, high=+np.inf, shape=(obs_dim),))
File "/usr/local/lib/python3.5/dist-packages/gym/spaces/box.py", line 33, in init
Space.init(self, shape, dtype)
File "/usr/local/lib/python3.5/dist-packages/gym/core.py", line 161, in init
self.shape = None if shape is None else tuple(shape)
TypeError: 'int' object is not iterable
I have found that I can overcome this particular error if I change (in environment.py row 68)
self.observation_space.append(spaces.Box(low=-np.inf, high=+np.inf, shape=(obs_dim),))
to
self.observation_space.append(spaces.Box(low=-np.inf, high=+np.inf, shape=(obs_dim,)))
However, then I start getting a whole new bunch of errors.
Any clue as to what to I am doing wrong?
When I use command bin/interactive.py --scenario simple.py, it turns out an error:
/Users/salse/Dropbox/openai/multiagent-particle-envs/multiagent/environment.py(149)_set_action()
148 import ipdb; ipdb.set_trace()
--> 149 action = [action]
150
The action seems undefined.
Also, could you give a example of training? I even don't know what to feed in env.step(). A single integer or a list of integer seems doesn't work.
value returned in _get_done function of MultiAgentEnv class in environment.py file should be 'self.done_callback(agent, self.world)'.
correct me if i am wrong.
Thanks
Python (3.5.4)
OpenAI gym (0.10.5)
tensorflow (1.8.0)
numpy (1.14.5)
maddpg code: https://github.com/openai/maddpg
I got an error when I run the maddpg code in the simple scene and display it
PS: 8 other environments can work
========================================
Step 1 : train and save model
$ python train.py --scenario simple_crypto
2020-01-22 17:46:58.755855: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Starting iterations...
steps: 24975, episodes: 1000, mean episode reward: -22.24953384943575, time: 26.487
steps: 49975, episodes: 2000, mean episode reward: -17.158019479170676, time: 43.373
steps: 74975, episodes: 3000, mean episode reward: -14.317337732475657, time: 43.159
steps: 99975, episodes: 4000, mean episode reward: -7.1827372802086815, time: 45.176
========================================
Step 2 : restore model and display
$ python train.py --scenario simple_crypto --display
2020-01-22 17:50:47.412262: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Loading previous state...
Starting iterations...
agent 1 to agent 0: B agent 2 to agent 0: C agent 0 to agent 1: A agent 2 to agent 1: C agent 0 to agent 2: A agent 1 to agent 2: B
Traceback (most recent call last):
File "train.py", line 211, in
train(arglist)
File "train.py", line 170, in train
env.render()
File "/Users/likejiao/Documents/LKJDocument/Code/github/openai/multiagent-particle-envs/multiagent/environment.py", line 234, in render
geom.set_color(*entity.color, alpha=0.5)
TypeError: set_color() got multiple values for argument 'alpha'
========================================
Is there something wrong with the simple_crypto scenario?
Traceback (most recent call last):
File "/home/shy/桌面/multiagent-particle-envs-master/bin/interactive.py", line 6, in
from multiagent.environment import MultiAgentEnv
File "/home/shy/桌面/multiagent-particle-envs-master/bin/../multiagent/environment.py", line 5, in
from multiagent.multi_discrete import MultiDiscrete
File "/home/shy/桌面/multiagent-particle-envs-master/bin/../multiagent/multi_discrete.py", line 7, in
from gym.spaces import prng
ImportError: cannot import name 'prng'
The action for each agent is Discrete(5). However actually it is Box(5) within (-1, 1).
The code here
agent.action.u[0] += action[0][1] - action[0][2] agent.action.u[1] += action[0][3] - action[0][4]
is used to get p_force and then to get p_vel, So what does action[0][0] do?
The reward of adversary agents for each step is based on is_collision which turns out to be the same reward for each adversary agent even if we consider the penalty in the case shape = True.
How is it different from self.shared_reward = True in environment.py?
I don't mean to complain, just wonder how it works.
Appreciate it if you guys could answer me.
Hi,
I find in the simple_world_comm environment that the obserbation function does not return the informaton you have defined such as food_pos, prey_forest, prey_forest_lead, and the comm variable is rewrited by comm = [world.agents[0].state.c], so comm does not contain communication of all other agents.
i wonder why and how to define the obserbation function
Hi,
I run some scenarios like simple tag and I can only observed a partial window(sometimes the agent run out of the window,I can only see a part of the whole 'world')
Crying for helping T T..
When I changed the simple.py in interactive.py to another scenario, the window is blank. How can I show those agents?
Hi,
I wanted to use these environment in combination with some other code that expects a newer version of gym. It seems that one of your latest updates adapted your codebase to function with gym 10.x, but in practice when running
pip install -e .
I get an error message compalining about gym version, I seems that the "multiagent" package still asks for gym==0.7.
pip._vendor.pkg_resources.ContextualVersionConflict: (gym 0.10.5 (/usr/local/lib/python3.5/dist-packages), Requirement.parse('gym==0.7'), {'multiagent'})
How can I overcome this?
def step(self, action_n):
...
reward_n = []
...
for agent in self.agents:
...
reward_n.append(self._get_reward(agent))
...
# all agents get total reward in cooperative case
reward = np.sum(reward_n)
if self.shared_reward: # simple tag is not shared reward
reward_n = [reward] * self.n
return ... reward_n, ...
For each agent, it will calculate its corresponding rewards. In the simple_tag.py
def adversary_reward(self, agent, world):
# Adversaries are rewarded for collisions with agents
rew = 0
shape = False
agents = self.good_agents(world)
adversaries = self.adversaries(world)
if shape: # reward can optionally be shaped (decreased reward for increased distance from agents)
for adv in adversaries:
rew -= 0.1 * min([np.sqrt(np.sum(np.square(a.state.p_pos - adv.state.p_pos))) for a in agents])
if agent.collide:
for ag in agents:
for adv in adversaries:
if self.is_collision(ag, adv):
rew += 10
return rew
agent.collide is always true. If the agent is an adversary agent, the coder iterates all adversary agents. The results in multiple adding of reward for all adversary agents.
Will be working on a forked repo to make this codebase compatible with Gym 0.13.1.
Are those environments compatible with OpenAI baselines implementation?
At first sights, it looks like the agents in openai/baselines don't support environments with an observable list.
For example the code below gives the exception:
~/tmp/baselines/baselines/deepq/deepq.py in learn(env, network, seed, lr, total_timesteps, buffer_size, exploration_fraction, exploration_final_eps, train_freq, batch_size, print_freq, checkpoint_freq, checkpoint_path, learning_starts, gamma, target_network_update_freq, prioritized_replay, prioritized_replay_alpha, prioritized_replay_beta0, prioritized_replay_beta_iters, prioritized_replay_eps, param_noise, callback, load_path, **network_kwargs)
202 make_obs_ph=make_obs_ph,
203 q_func=q_func,
--> 204 num_actions=env.action_space.n,
205 optimizer=tf.train.AdamOptimizer(learning_rate=lr),
206 gamma=gamma,
AttributeError: 'list' object has no attribute 'n'
Code that instantiates a baseline agent with a multiagent environment:
from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
from baselines.run import get_learn_function
from multiagent.environment import MultiAgentEnv
import multiagent.scenarios as scenarios
common_kwargs = dict(total_timesteps=30000, network="mlp", gamma=1.0, seed=0)
learn_kwargs = {
'a2c' : dict(nsteps=32, value_network='copy', lr=0.05),
'acktr': dict(nsteps=32, value_network='copy'),
'deepq': dict(total_timesteps=20000),
'ppo2': dict(value_network='copy'),
'trpo_mpi': {}
}
alg = "deepq"
kwargs = common_kwargs.copy()
kwargs.update(learn_kwargs[alg])
learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs)
def env_fn():
scenario = scenarios.load("simple_tag.py").Scenario()
world = scenario.make_world()
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, scenario.benchmark_data)
return env
env = SubprocVecEnv([env_fn])
model = learn_fn(env)
We are trying to add boundaries to the environment, but when we check the code of the entity in core.py, there is only the size property, from our understanding, we can only create circle object. Are there any solutions to create other shape?
I'm trying to figure out what the World.dim_c paramter does, does anyone know?
It is defined in core.py here:
# multi-agent world
class World(object):
def __init__(self):
# list of agents and entities (can change at execution-time!)
self.agents = []
self.landmarks = []
# communication channel dimensionality
self.dim_c = 0
Hi, I am using your environment for mutliagent algorithm's test.
Can I get some more information about observation,, action, reward's information of environments you provide. I have been using simple push environment.
run interactive.py have error
ctypes.ArgumentError: argument 2: <class 'TypeError'>: wrong type
While you can see pictures,not animations
when I executed
python bin/interactive.py -s simple_push.py
It only showed 2 empty windows
The only scenerio that worked is simple.py
How can I solve this ?
I see the env.step() function returns the observation of all the agents. Is there any way to get global state information?
Is it possible to run this project using the spyder?/ Otherwise, what is an easy tool? I have just started learning about this. Please explain how to run it. I import all the dependencies.
Is there any particular reason why the _step(self,action_n) function in environment.py just returns False for done_n for all agents, irrespective of what is happening in the environment?
Makes it very difficult to train MADDPG, because initially the agents just keep going out of the environment (the render window), and while I believe the environment should return done=True in this case, allowing the training to reset and start over, it just keeps going - making it difficult to learn IMO.
Another question is why are the environments not bounded?
flake8 testing of https://github.com/openai/multiagent-particle-envs on Python 2.7.14
$ flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics
./make_env.py:41:97: F821 undefined name 'arglist'
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, arglist, scenario.benchmark_data)
^
./make_env.py:43:97: F821 undefined name 'arglist'
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, arglist)
^
./multiagent/policy.py:40:44: F821 undefined name 'envo'
return np.concatenate([u. np.zeros(envo.world.dim_c)])
^
./multiagent/scenarios/simple_speaker_listener.py:60:16: F821 undefined name 'reward'
return reward(agent, reward)
^
./multiagent/scenarios/simple_speaker_listener.py:60:30: F821 undefined name 'reward'
return reward(agent, reward)
^
5 F821 undefined name 'arglist'
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