Quentin Gallouédec
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Browse files- .gitattributes +1 -0
- README.md +81 -0
- a2c-ReacherBulletEnv-v0.zip +3 -0
- a2c-ReacherBulletEnv-v0/_stable_baselines3_version +1 -0
- a2c-ReacherBulletEnv-v0/data +106 -0
- a2c-ReacherBulletEnv-v0/policy.optimizer.pth +3 -0
- a2c-ReacherBulletEnv-v0/policy.pth +3 -0
- a2c-ReacherBulletEnv-v0/pytorch_variables.pth +3 -0
- a2c-ReacherBulletEnv-v0/system_info.txt +7 -0
- args.yml +79 -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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@@ -32,3 +32,4 @@ 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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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst 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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README.md
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---
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library_name: stable-baselines3
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tags:
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- ReacherBulletEnv-v0
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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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- 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: ReacherBulletEnv-v0
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type: ReacherBulletEnv-v0
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metrics:
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- type: mean_reward
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value: 3.06 +/- 11.48
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name: mean_reward
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verified: false
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---
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# **A2C** Agent playing **ReacherBulletEnv-v0**
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This is a trained model of a **A2C** agent playing **ReacherBulletEnv-v0**
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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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Install the RL Zoo (with SB3 and SB3-Contrib):
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```bash
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pip install rl_zoo3
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```
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```
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# Download model and save it into the logs/ folder
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python -m rl_zoo3.load_from_hub --algo a2c --env ReacherBulletEnv-v0 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo a2c --env ReacherBulletEnv-v0 -f logs/
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```
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If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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```
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python -m rl_zoo3.load_from_hub --algo a2c --env ReacherBulletEnv-v0 -orga qgallouedec -f logs/
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python -m rl_zoo3.enjoy --algo a2c --env ReacherBulletEnv-v0 -f logs/
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```
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## Training (with the RL Zoo)
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```
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python -m rl_zoo3.train --algo a2c --env ReacherBulletEnv-v0 -f logs/
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# Upload the model and generate video (when possible)
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python -m rl_zoo3.push_to_hub --algo a2c --env ReacherBulletEnv-v0 -f logs/ -orga qgallouedec
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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_0.0008'),
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('max_grad_norm', 0.5),
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('n_envs', 4),
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('n_steps', 8),
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('n_timesteps', 2000000.0),
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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-ReacherBulletEnv-v0.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:3dfbcfc3c9702986bc1a06e4561942ac8b7bd50ebe238fd0db868244907bc61f
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size 106574
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a2c-ReacherBulletEnv-v0/_stable_baselines3_version
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1.8.0a6
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a2c-ReacherBulletEnv-v0/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f74bf090d30>",
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f74bf090dc0>",
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+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f74bf090e50>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f74bf090ee0>",
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"_build": "<function ActorCriticPolicy._build at 0x7f74bf090f70>",
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"forward": "<function ActorCriticPolicy.forward at 0x7f74bf092040>",
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"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f74bf0920d0>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f74bf092160>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7f74bf0921f0>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f74bf092280>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f74bf092310>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f74bf0923a0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7f74bf0916c0>"
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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:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/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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"_shape": [
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9
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],
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"low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf]",
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"high": "[inf inf inf inf inf inf inf inf inf]",
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"bounded_below": "[False False False False False False False False False]",
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"bounded_above": "[False False False False False False False False False]",
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"_np_random": null
|
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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-ReacherBulletEnv-v0/policy.optimizer.pth
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a2c-ReacherBulletEnv-v0/pytorch_variables.pth
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a2c-ReacherBulletEnv-v0/system_info.txt
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- OS: Linux-5.19.0-32-generic-x86_64-with-glibc2.35 # 33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Jan 30 17:03:34 UTC 2
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|
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args.yml
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|
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|
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|
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|
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config.yml
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|
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|
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|
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|
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|
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- - policy
|
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|
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|
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|
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- - use_rms_prop
|
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- true
|
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- - use_sde
|
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|
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|
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env_kwargs.yml
ADDED
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1 |
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{}
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replay.mp4
ADDED
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version https://git-lfs.github.com/spec/v1
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size 64408
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results.json
ADDED
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|
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{"mean_reward": 3.0624914, "std_reward": 11.481656516473851, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-27T14:56:09.282592"}
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train_eval_metrics.zip
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