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View Code? Open in Web Editor NEWA framework for multi-agent reinforcement learning.
License: MIT License
A framework for multi-agent reinforcement learning.
License: MIT License
A brief docstring at the top of each module would make it easier for newbies to navigate the codebase.
At master (526df2a), on Linux, Python 3.8.7, installed packages:
absl-py==0.11.0
astunparse==1.6.3
cachetools==4.2.0
certifi==2020.12.5
chardet==3.0.4
click==7.1.2
Flask==1.1.2
flatbuffers==1.12
gast==0.3.3
google-auth==1.24.0
google-auth-oauthlib==0.4.2
google-pasta==0.2.0
-e git+https://github.com/cool-RR/grid_royale@526df2a01fa8b0ba39423a6977929539c451673a#egg=grid_royale
grpcio==1.32.0
h5py==2.10.0
idna==2.10
immutabledict==1.2.0
itsdangerous==1.1.0
Jinja2==2.11.2
Keras==2.4.3
Keras-Preprocessing==1.1.2
Markdown==3.3.3
MarkupSafe==1.1.1
more-itertools==8.6.0
numpy==1.19.5
oauthlib==3.1.0
opt-einsum==3.3.0
protobuf==3.14.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
PyYAML==5.4.1
requests==2.24.0
requests-oauthlib==1.3.0
rsa==4.7
scipy==1.6.0
six==1.15.0
tensorboard==2.4.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.4.1
tensorflow-estimator==2.4.0
termcolor==1.1.0
typing-extensions==3.7.4.3
urllib3==1.25.11
Werkzeug==1.0.1
wrapt==1.12.1
Starting server emits uncaught error:
(venv) zhuyifei1999@zhuyifei1999-ThinkPad-T480 /tmp/grid_royale $ grid_royale play
Traceback (most recent call last):
File "/tmp/grid_royale/venv/bin/grid_royale", line 33, in <module>
sys.exit(load_entry_point('grid-royale', 'console_scripts', 'grid_royale')())
File "/tmp/grid_royale/venv/bin/grid_royale", line 25, in importlib_load_entry_point
return next(matches).load()
File "/usr/lib/python3.8/importlib/metadata.py", line 77, in load
module = import_module(match.group('module'))
File "/usr/lib/python3.8/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
File "<frozen importlib._bootstrap>", line 991, in _find_and_load
File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 671, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 783, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/tmp/grid_royale/grid_royale/__init__.py", line 9, in <module>
from .base import *
File "/tmp/grid_royale/grid_royale/base.py", line 74, in <module>
from . import utils
File "/tmp/grid_royale/grid_royale/utils/__init__.py", line 53, in <module>
copyreg.pickle(tensorflow.python._tf_stack.StackSummary, pickle_stack_summary)
AttributeError: module 'tensorflow' has no attribute 'python'
Then get the reward from that. Maybe we need a few iterations of that.
It'll simplify the logic if all actions were considered legal. Right now the only actions that are not considered legal are walking toward a wall. We should make that a legal action, and just have that count as a collision with the usual negative reward.
Hey I made a interactive online documentation for your Grid_Royale project.
It is hosted at Website
Hello there
Im very intrigued by the idea of this project. I was thinking that maybe the core logic of it is a bit too "biological" for being basis of "social" dynamics.
Don't get me wrong in being sentimental, let me explain what i mean. Of course the struggle to avoid "death" is encoded in the nature of any biological and living thing. And many social behaviors have emerged solely out if the struggle to survive or effectiveness of living in social groups.
But, joining a gang is a fatal decision that has been made by millions throughout history of mankind. The average age of death of gang members is 23 or something like that. The point is that decisions that individuals make might be recurring among individuals even if those decisions are guaranteed to kill you, one way or the other.
Requesting as per sprint discussion - these rules currently aren't written up?
Add an action to build a wall next to the player. A wall can't be passed by a creature, walking into it results in collision damage. It must be shot with a bullet.
This can be conducive to territory-building.
There's a bug I observed: When there are a bunch of bullets one after another, and the player walks towards them, sometimes some of the bullets don't hit the player, but skip past it. Figure it out and fix it.
It would be cool if a command line flag could disable all shooting in the game, to make it just about eating food and avoiding collisions. There's some interesting behavior there.
Calling predict
for each observation separately is a huge time-sink. We should batch these as much as possible. All creatures of the same strategy should batch their call to predict
and then split the result up. Maybe we can also run multiple games in parallel for training.
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