Antonin Raffin
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Browse files- .gitattributes +2 -0
- README.md +69 -0
- a2c-BipedalWalker-v3.zip +3 -0
- a2c-BipedalWalker-v3/_stable_baselines3_version +1 -0
- a2c-BipedalWalker-v3/data +106 -0
- a2c-BipedalWalker-v3/policy.optimizer.pth +3 -0
- a2c-BipedalWalker-v3/policy.pth +3 -0
- a2c-BipedalWalker-v3/pytorch_variables.pth +3 -0
- a2c-BipedalWalker-v3/system_info.txt +7 -0
- args.yml +59 -0
- config.yml +31 -0
- env_kwargs.yml +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
- train_eval_metrics.zip +3 -0
- vec_normalize.pkl +3 -0
.gitattributes
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@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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vec_normalize.pkl filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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library_name: stable-baselines3
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tags:
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- BipedalWalker-v3
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: A2C
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results:
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- metrics:
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- type: mean_reward
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value: 300.85 +/- 1.44
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name: mean_reward
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task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: BipedalWalker-v3
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type: BipedalWalker-v3
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---
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# **A2C** Agent playing **BipedalWalker-v3**
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This is a trained model of a **A2C** agent playing **BipedalWalker-v3**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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The RL Zoo is a training framework for Stable Baselines3
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reinforcement learning agents,
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with hyperparameter optimization and pre-trained agents included.
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## Usage (with SB3 RL Zoo)
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RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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```
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# Download model and save it into the logs/ folder
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python -m utils.load_from_hub --algo a2c --env BipedalWalker-v3 -orga sb3 -f logs/
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python enjoy.py --algo a2c --env BipedalWalker-v3 -f logs/
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```
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## Training (with the RL Zoo)
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```
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python train.py --algo a2c --env BipedalWalker-v3 -f logs/
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# Upload the model and generate video (when possible)
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python -m utils.push_to_hub --algo a2c --env BipedalWalker-v3 -f logs/ -orga sb3
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```
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## Hyperparameters
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```python
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OrderedDict([('ent_coef', 0.0),
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('gae_lambda', 0.9),
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('gamma', 0.99),
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('learning_rate', 'lin_0.00096'),
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('max_grad_norm', 0.5),
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('n_envs', 16),
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('n_steps', 8),
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('n_timesteps', 5000000.0),
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('normalize', True),
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('normalize_advantage', False),
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('policy', 'MlpPolicy'),
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('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'),
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('use_rms_prop', True),
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('use_sde', True),
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('vf_coef', 0.4),
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('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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```
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a2c-BipedalWalker-v3.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:1347120e5f37521e08163027a83205ed929a1b9644a58280825d9f0f96901674
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size 130095
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a2c-BipedalWalker-v3/_stable_baselines3_version
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1.5.1a8
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a2c-BipedalWalker-v3/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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"__module__": "stable_baselines3.common.policies",
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"__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
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"__init__": "<function ActorCriticPolicy.__init__ at 0x7f4193176950>",
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f41931769e0>",
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4193176a70>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4193176b00>",
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"_build": "<function ActorCriticPolicy._build at 0x7f4193176b90>",
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"forward": "<function ActorCriticPolicy.forward at 0x7f4193176c20>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4193176cb0>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7f4193176d40>",
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+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4193176dd0>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4193176e60>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4193176ef0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc_data object at 0x7f41931c7840>"
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},
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+
"verbose": 1,
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"policy_kwargs": {
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":type:": "<class 'dict'>",
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":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
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"log_std_init": -2,
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"ortho_init": false,
|
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+
"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
|
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+
"optimizer_kwargs": {
|
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"alpha": 0.99,
|
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+
"eps": 1e-05,
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+
"weight_decay": 0
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}
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},
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+
"observation_space": {
|
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":type:": "<class 'gym.spaces.box.Box'>",
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":serialized:": "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",
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"dtype": "float32",
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"low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
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"high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf]",
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"bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False]",
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"bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False]",
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"_np_random": null,
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"_shape": [
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24
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]
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},
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"action_space": {
|
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":type:": "<class 'gym.spaces.box.Box'>",
|
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a2c-BipedalWalker-v3/policy.optimizer.pth
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2 |
+
Python: 3.7.10
|
3 |
+
Stable-Baselines3: 1.5.1a8
|
4 |
+
PyTorch: 1.11.0
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.2
|
7 |
+
Gym: 0.21.0
|
args.yml
ADDED
@@ -0,0 +1,59 @@
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|
|
1 |
+
!!python/object/apply:collections.OrderedDict
|
2 |
+
- - - algo
|
3 |
+
- a2c
|
4 |
+
- - env
|
5 |
+
- BipedalWalker-v3
|
6 |
+
- - env_kwargs
|
7 |
+
- null
|
8 |
+
- - eval_episodes
|
9 |
+
- 10
|
10 |
+
- - eval_freq
|
11 |
+
- 10000
|
12 |
+
- - gym_packages
|
13 |
+
- []
|
14 |
+
- - hyperparams
|
15 |
+
- null
|
16 |
+
- - log_folder
|
17 |
+
- rl-trained-agents/
|
18 |
+
- - log_interval
|
19 |
+
- -1
|
20 |
+
- - n_evaluations
|
21 |
+
- 20
|
22 |
+
- - n_jobs
|
23 |
+
- 1
|
24 |
+
- - n_startup_trials
|
25 |
+
- 10
|
26 |
+
- - n_timesteps
|
27 |
+
- -1
|
28 |
+
- - n_trials
|
29 |
+
- 10
|
30 |
+
- - num_threads
|
31 |
+
- -1
|
32 |
+
- - optimize_hyperparameters
|
33 |
+
- false
|
34 |
+
- - pruner
|
35 |
+
- median
|
36 |
+
- - sampler
|
37 |
+
- tpe
|
38 |
+
- - save_freq
|
39 |
+
- -1
|
40 |
+
- - save_replay_buffer
|
41 |
+
- false
|
42 |
+
- - seed
|
43 |
+
- 3598227847
|
44 |
+
- - storage
|
45 |
+
- null
|
46 |
+
- - study_name
|
47 |
+
- null
|
48 |
+
- - tensorboard_log
|
49 |
+
- ''
|
50 |
+
- - trained_agent
|
51 |
+
- ''
|
52 |
+
- - truncate_last_trajectory
|
53 |
+
- true
|
54 |
+
- - uuid
|
55 |
+
- false
|
56 |
+
- - vec_env
|
57 |
+
- subproc
|
58 |
+
- - verbose
|
59 |
+
- 1
|
config.yml
ADDED
@@ -0,0 +1,31 @@
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
!!python/object/apply:collections.OrderedDict
|
2 |
+
- - - ent_coef
|
3 |
+
- 0.0
|
4 |
+
- - gae_lambda
|
5 |
+
- 0.9
|
6 |
+
- - gamma
|
7 |
+
- 0.99
|
8 |
+
- - learning_rate
|
9 |
+
- lin_0.00096
|
10 |
+
- - max_grad_norm
|
11 |
+
- 0.5
|
12 |
+
- - n_envs
|
13 |
+
- 16
|
14 |
+
- - n_steps
|
15 |
+
- 8
|
16 |
+
- - n_timesteps
|
17 |
+
- 5000000.0
|
18 |
+
- - normalize
|
19 |
+
- true
|
20 |
+
- - normalize_advantage
|
21 |
+
- false
|
22 |
+
- - policy
|
23 |
+
- MlpPolicy
|
24 |
+
- - policy_kwargs
|
25 |
+
- dict(log_std_init=-2, ortho_init=False)
|
26 |
+
- - use_rms_prop
|
27 |
+
- true
|
28 |
+
- - use_sde
|
29 |
+
- true
|
30 |
+
- - vf_coef
|
31 |
+
- 0.4
|
env_kwargs.yml
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{}
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:61c185675a76aac58f416dbdc9498499d4b504cd74b4ea3c11c2f8fe85ceba6c
|
3 |
+
size 434169
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 300.8528041999999, "std_reward": 1.4428884079717876, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-02T18:06:00.128574"}
|
train_eval_metrics.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:73b8b2e1247db5816f83a3c811ccb80f71fc87132e41ed4be3ea930353619467
|
3 |
+
size 132197
|
vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:32daf605898f4b73839fd068d54c4535d603ea9233cbde2c56e1657348640d28
|
3 |
+
size 8055
|