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License: MIT License
TensorFlow2 Reinforcement Learning
License: MIT License
Replace custom distribution classes under tf2rl/distributions with TensorFlow Probability
Labels of TensorBoard output depend on names of results directories, so readability improves when user can specify the names.
Generally, RL collects experiences by interacting with environment, and it needs to generate an action using current policy networks.
However, making an action for each transition is not computationally efficient because the input to the neural network uses only one batch. So, there could be room to improve efficiency by changing batch size.
In this issue, try to make batch size bigger for one actions by preparing multiple environments and forward all environments with one actions.
Note that this method is only valid for no-GIL environments, because python only works on one process.
tf.train.create_global_step()
related operations
4.0 while total_steps < self._max_steps:
4.8 if total_steps >= self._policy.n_warmup:
4.8 if total_steps >= self._policy.n_warmup:
2.6 if int(total_steps) % self._model_save_interval == 0:
$ git checkout cff2d42ae73b7ddaa050853b3359a78ada06929a
$ python examples/run_dqn_line_profiler.py --max-steps=10000
...
File: /Users/keiohta/workspace/rl/tf2rl/tf2rl/experiments/trainer.py
Function: call at line 50
Line # Hits Time Per Hit % Time Line Contents
==============================================================
50 def call(self):
51 1 3666.0 3666.0 0.0 total_steps = tf.train.create_global_step()
52 1 2.0 2.0 0.0 episode_steps = 0
53 1 0.0 0.0 0.0 episode_return = 0
54 1 2.0 2.0 0.0 episode_start_time = time.time()
55 1 1.0 1.0 0.0 n_episode = 0
56
57 1 1.0 1.0 0.0 replay_buffer = get_replay_buffer(
58 1 1.0 1.0 0.0 self._policy, self._env, self._use_prioritized_rb,
59 1 762.0 762.0 0.0 self._use_nstep_rb, self._n_step)
60
61 1 72.0 72.0 0.0 obs = self._env.reset()
62
63 1 1049.0 1049.0 0.0 with tf.contrib.summary.record_summaries_every_n_global_steps(1000):
64 10001 670392.0 67.0 4.0 while total_steps < self._max_steps:
65 10000 629217.0 62.9 3.8 if total_steps < self._policy.n_warmup:
66 500 3332.0 6.7 0.0 action = self._env.action_space.sample()
67 else:
68 9500 2394183.0 252.0 14.4 action = self._policy.get_action(obs)
69
70 10000 285089.0 28.5 1.7 next_obs, reward, done, _ = self._env.step(action)
71 10000 9598.0 1.0 0.1 if self._show_progress:
72 self._env.render()
73 10000 7443.0 0.7 0.0 episode_steps += 1
74 10000 7514.0 0.8 0.0 episode_return += reward
75 10000 768571.0 76.9 4.6 total_steps.assign_add(1)
76
77 10000 10090.0 1.0 0.1 done_flag = done
78 10000 12436.0 1.2 0.1 if hasattr(self._env, "_max_episode_steps") and \
79 10000 8542.0 0.9 0.1 episode_steps == self._env._max_episode_steps:
80 6 5.0 0.8 0.0 done_flag = False
81 10000 310069.0 31.0 1.9 replay_buffer.add(obs=obs, act=action, next_obs=next_obs, rew=reward, done=done_flag)
82 10000 9355.0 0.9 0.1 obs = next_obs
83
84 10000 9105.0 0.9 0.1 if done or episode_steps == self._episode_max_steps:
85 179 2326.0 13.0 0.0 obs = self._env.reset()
86
87 179 199.0 1.1 0.0 n_episode += 1
88 179 268.0 1.5 0.0 fps = episode_steps / (time.time() - episode_start_time)
89 179 266.0 1.5 0.0 self.logger.info("Total Epi: {0: 5} Steps: {1: 7} Episode Steps: {2: 5} Return: {3: 5.4f} FPS: {4:5.2f}".format(
90 179 30602.0 171.0 0.2 n_episode, int(total_steps), episode_steps, episode_return, fps))
91
92 179 211.0 1.2 0.0 episode_steps = 0
93 179 122.0 0.7 0.0 episode_return = 0
94 179 173.0 1.0 0.0 episode_start_time = time.time()
95
96 10000 804974.0 80.5 4.8 if total_steps >= self._policy.n_warmup:
97 9501 371540.0 39.1 2.2 samples = replay_buffer.sample(self._policy.batch_size)
98 9501 11071.0 1.2 0.1 td_error = self._policy.train(
99 9501 8588.0 0.9 0.1 samples["obs"], samples["act"], samples["next_obs"],
100 9501 29581.0 3.1 0.2 samples["rew"], np.array(samples["done"], dtype=np.float64),
101 9501 8322550.0 876.0 49.9 None if not self._use_prioritized_rb else samples["weights"])
102 9501 14172.0 1.5 0.1 if self._use_prioritized_rb:
103 replay_buffer.update_priorities(samples["indexes"], np.abs(td_error) + 1e-6)
104 9501 487314.0 51.3 2.9 if int(total_steps) % self._test_interval == 0:
105 5 86.0 17.2 0.0 with tf.contrib.summary.always_record_summaries():
106 5 989351.0 197870.2 5.9 avg_test_return = self.evaluate_policy(int(total_steps))
107 5 20.0 4.0 0.0 self.logger.info("Evaluation Total Steps: {0: 7} Average Reward {1: 5.4f} over {2: 2} episodes".format(
108 5 1098.0 219.6 0.0 int(total_steps), avg_test_return, self._test_episodes))
109 5 1452.0 290.4 0.0 tf.contrib.summary.scalar(name="AverageTestReturn", tensor=avg_test_return, family="loss")
110 5 1288.0 257.6 0.0 tf.contrib.summary.scalar(name="FPS", tensor=fps, family="loss")
111
112 5 214.0 42.8 0.0 self.writer.flush()
113
114 10000 433524.0 43.4 2.6 if int(total_steps) % self._model_save_interval == 0:
115 1 16610.0 16610.0 0.1 self.checkpoint_manager.save()
116
117 1 44.0 44.0 0.0 tf.contrib.summary.flush()
So far, all operations are done with float64 (np.float64
and tf.float64
) for compatibility with cpprb
, but cpprb
now experimentally supports arbitrary data type, so replace all float64 operations with float32 operations to accelerate computation.
Line 140 in c86d944
ApeX samples randomly from local replay buffer instead of copying all the data.
This looks like unwilling bug.
tf2rl does not keep up to date with recent cpprb development.
cpprb >= 7.14 obtained N-step feature in the new experimental
package.
cpprb >= 8.0 replace stable code with experimental
one, finally.
Although training with clipped rewards, test scores should be recorded with raw scores as many papers do
Visualize final game capture of an episode so that user can know the game progress.
Call following function to visualize the capture to TensorBoard.
tf.contrib.summary.image('train/input_img', tf.cast(image * 255.0, tf.uint8))
Requirement の説明がないため、インストール方法を試してExampleを実行しても、エラーになる。
必要な依存関係を記載するもしくは、setup.py によってインストールすることが望ましい。
Implement automatic tuning of temperature parameter of entropy and reproduce results from Soft Actor-Critic Algorithms and Applications.
A depending library cpprb is scheduled to break its api in version 8.
(ReplayBuffer
s in cpprb
namespaces are replaced with those in cpprb.experimental
, filnally.)
To prepare migration, the current version of tf2rl is recommended to ensure to use version 7 by specifying in setup.py
hi, may I know which paper(s)/method the discrete SAC is based upon?
from my understanding there are 3 main implementations of discrete SAC:
but they all include the auto entropy tuning/temperature term. I may have missed it but I dont see it in your version of the code. Thanks for your time!
Hi,
I noticed another thing. In DDPG
implementation, there is a method get_action()
that I think by accident doesn't add noise to action in training phase, but adds it during testing. Here's the exact line that I think is problematic:
Line 100 in 1badc2a
As per original pseudocode in original paper DDPG, page 5, it is explicitly stated that noise is added during training. I'm assuming actor's action is then directly used during testing.
Distributed Prioritized Experience Replay
Hyper parameters are shown in appendix D
Agent type can be classified into discrete
or continuous
.
Also, some detail information should be written, such as recurrent output.
Coverage badge is unknown for some reasons. Fix this.
Currently just use the specified value, such as epsilon=0.1
.
Currently users need to specify hyper parameters by passing command line arguments or set_defaults
in examples/run_*.py
, but it's a bother to do for each algorithm/environment. So, use gin
to specify initial values especially for reproducing the results of papers.
https://github.com/google/gin-config
以下、実行時にコンソールに表示された警告メッセージ
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
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