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ppo-HumanoidStandup-v2-2078523688

Reinforcement Learning
stable-baselines3
HumanoidStandup-v2
deep-reinforcement-learning
HumanoidStandup-v4
Eval Results
Model card Files Community
Quentin Gallouédec commited on
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  1. .gitattributes +1 -0
  2. README.md +83 -0
  3. args.yml +83 -0
  4. config.yml +32 -0
  5. env_kwargs.yml +1 -0
  6. ppo-HumanoidStandup-v2.zip +3 -0
  7. ppo-HumanoidStandup-v2/_stable_baselines3_version +1 -0
  8. ppo-HumanoidStandup-v2/data +119 -0
  9. ppo-HumanoidStandup-v2/policy.optimizer.pth +3 -0
  10. ppo-HumanoidStandup-v2/policy.pth +3 -0
  11. ppo-HumanoidStandup-v2/pytorch_variables.pth +3 -0
  12. ppo-HumanoidStandup-v2/system_info.txt +7 -0
  13. replay.mp4 +3 -0
  14. results.json +1 -0
  15. train_eval_metrics.zip +3 -0
  16. vec_normalize.pkl +3 -0
.gitattributes CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
@@ -0,0 +1,83 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - HumanoidStandup-v2
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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: PPO
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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: HumanoidStandup-v2
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+ type: HumanoidStandup-v2
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+ metrics:
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+ - type: mean_reward
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+ value: 156095.14 +/- 6538.73
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **PPO** Agent playing **HumanoidStandup-v2**
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+ This is a trained model of a **PPO** agent playing **HumanoidStandup-v2**
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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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+
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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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+
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+ ## Usage (with SB3 RL Zoo)
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+
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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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+ 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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+ ```
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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 ppo --env HumanoidStandup-v2 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo ppo --env HumanoidStandup-v2 -f logs/
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+ ```
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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 ppo --env HumanoidStandup-v2 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo ppo --env HumanoidStandup-v2 -f logs/
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+ ```
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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 ppo --env HumanoidStandup-v2 -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 ppo --env HumanoidStandup-v2 -f logs/ -orga qgallouedec
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+ ```
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+
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+ ## Hyperparameters
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+ ```python
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+ OrderedDict([('batch_size', 32),
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+ ('clip_range', 0.3),
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+ ('ent_coef', 3.62109e-06),
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+ ('gae_lambda', 0.9),
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+ ('gamma', 0.99),
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+ ('learning_rate', 2.55673e-05),
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+ ('max_grad_norm', 0.7),
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+ ('n_envs', 1),
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+ ('n_epochs', 20),
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+ ('n_steps', 512),
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+ ('n_timesteps', 10000000.0),
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+ ('normalize', True),
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+ ('policy', 'MlpPolicy'),
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+ ('policy_kwargs',
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+ 'dict( log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, '
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+ 'net_arch=dict(pi=[256, 256], vf=[256, 256]) )'),
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+ ('vf_coef', 0.430793),
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+ ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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+ ```
args.yml ADDED
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+ - []
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+ - false
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+ - - storage
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+ - null
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+ - - study_name
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+ - runs/HumanoidStandup-v2__ppo__2078523688__1676767898
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+ - - track
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+ - true
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+ - - trained_agent
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+ - ''
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+ - sb3
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+ - - wandb_tags
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+ - []
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+ - - yaml_file
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+ - null
config.yml ADDED
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+ !!python/object/apply:collections.OrderedDict
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+ - - - batch_size
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+ - 0.7
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+ 256], vf=[256, 256]) )
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+ - - vf_coef
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+ - 0.430793
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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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