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ppo-BipedalWalker-v3

Reinforcement Learning
stable-baselines3
BipedalWalker-v3
deep-reinforcement-learning
Eval Results
Model card Files Community
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Commit
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Files changed (12) hide show
  1. README.md +6 -6
  2. args.yml +29 -9
  3. config.yml +5 -5
  4. ppo-BipedalWalker-v3.zip +2 -2
  5. ppo-BipedalWalker-v3/data +40 -41
  6. ppo-BipedalWalker-v3/policy.optimizer.pth +2 -2
  7. ppo-BipedalWalker-v3/policy.pth +2 -2
  8. ppo-BipedalWalker-v3/system_info.txt +1 -1
  9. replay.mp4 +2 -2
  10. results.json +1 -1
  11. train_eval_metrics.zip +2 -2
  12. vec_normalize.pkl +2 -2
README.md CHANGED
@@ -10,7 +10,7 @@ model-index:
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  results:
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  - metrics:
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  - type: mean_reward
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- value: 149.15 +/- 162.01
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  name: mean_reward
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  task:
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  type: reinforcement-learning
@@ -51,12 +51,12 @@ python -m utils.push_to_hub --algo ppo --env BipedalWalker-v3 -f logs/ -orga sb3
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  ## Hyperparameters
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  ```python
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  OrderedDict([('batch_size', 64),
54
- ('clip_range', 0.2),
55
- ('ent_coef', 0.001),
56
  ('gae_lambda', 0.95),
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- ('gamma', 0.99),
58
- ('learning_rate', 0.00025),
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- ('n_envs', 16),
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  ('n_epochs', 10),
61
  ('n_steps', 2048),
62
  ('n_timesteps', 5000000.0),
 
10
  results:
11
  - metrics:
12
  - type: mean_reward
13
+ value: 288.30 +/- 2.23
14
  name: mean_reward
15
  task:
16
  type: reinforcement-learning
 
51
  ## Hyperparameters
52
  ```python
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  OrderedDict([('batch_size', 64),
54
+ ('clip_range', 0.18),
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+ ('ent_coef', 0.0),
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  ('gae_lambda', 0.95),
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+ ('gamma', 0.999),
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+ ('learning_rate', 0.0003),
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+ ('n_envs', 32),
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  ('n_epochs', 10),
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  ('n_steps', 2048),
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  ('n_timesteps', 5000000.0),
args.yml CHANGED
@@ -1,24 +1,34 @@
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  !!python/object/apply:collections.OrderedDict
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  - - - algo
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  - ppo
 
 
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  - - env
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  - BipedalWalker-v3
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  - - env_kwargs
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  - - log_folder
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  - - log_interval
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  - -1
 
 
 
 
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  - - n_evaluations
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- - 20
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  - - n_jobs
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  - 1
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  - - n_startup_trials
@@ -26,9 +36,13 @@
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  - - n_timesteps
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  - -1
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  - - n_trials
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- - 10
 
 
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  - - num_threads
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  - -1
 
 
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  - - optimize_hyperparameters
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  - false
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  - - pruner
@@ -40,20 +54,26 @@
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  - - save_replay_buffer
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  - false
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  - - seed
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  - - storage
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  - null
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- - ''
 
 
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  - - vec_env
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  - dummy
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  - - verbose
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  - 1
 
 
 
 
 
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  !!python/object/apply:collections.OrderedDict
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  - - - algo
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  - ppo
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  - - env
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  - BipedalWalker-v3
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  - - env_kwargs
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  - null
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  - - eval_episodes
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  - - eval_freq
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  - - gym_packages
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  - []
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  - - hyperparams
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+ - clip_range: 0.18
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+ ent_coef: 0.0
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+ gamma: 0.999
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+ learning_rate: 0.0003
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+ n_envs: 32
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  - - log_folder
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+ - logs/
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  - - log_interval
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  - -1
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  - - pruner
 
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  - - save_replay_buffer
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  - - storage
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config.yml CHANGED
@@ -2,17 +2,17 @@
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  - - - batch_size
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  - - clip_range
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  - - ent_coef
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ppo-BipedalWalker-v3.zip CHANGED
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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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  },
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- "target_kl": null,
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- }
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  }
 
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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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7fccc6e3f950>",
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+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fccc6e3f9e0>",
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+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fccc6e3fa70>",
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+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fccc6e3fb00>",
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+ "_build": "<function ActorCriticPolicy._build at 0x7fccc6e3fb90>",
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+ "forward": "<function ActorCriticPolicy.forward at 0x7fccc6e3fc20>",
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+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fccc6e3fcb0>",
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+ "_predict": "<function ActorCriticPolicy._predict at 0x7fccc6e3fd40>",
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+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fccc6e3fdd0>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fccc6e3fe60>",
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+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fccc6e3fef0>",
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  "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7fccc6e91810>"
20
  },
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  "verbose": 1,
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  "policy_kwargs": {},
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  "observation_space": {
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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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  },
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  "action_space": {
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  "bounded_below": "[ True True True True]",
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  "bounded_above": "[ True True True True]",
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+ "_np_random": "RandomState(MT19937)"
 
 
 
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  },
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+ "n_envs": 32,
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  },
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