Initial commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 1185.21 +/- 557.92
+      value: 3034.50 +/- 1124.70
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -37,15 +37,21 @@ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
 
 ```
 # Download model and save it into the logs/ folder
-python -m utils.load_from_hub --algo ppo --env seals/Ant-v0 -orga HumanCompatibleAI -f logs/
+python -m rl_zoo3.load_from_hub --algo ppo --env seals/Ant-v0 -orga HumanCompatibleAI -f logs/
 python enjoy.py --algo ppo --env seals/Ant-v0  -f logs/
 ```
 
+If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
+```
+python -m rl_zoo3.load_from_hub --algo ppo --env seals/Ant-v0 -orga HumanCompatibleAI -f logs/
+rl_zoo3 enjoy --algo ppo --env seals/Ant-v0  -f logs/
+```
+
 ## Training (with the RL Zoo)
 ```
 python train.py --algo ppo --env seals/Ant-v0 -f logs/
 # Upload the model and generate video (when possible)
-python -m utils.push_to_hub --algo ppo --env seals/Ant-v0 -f logs/ -orga HumanCompatibleAI
+python -m rl_zoo3.push_to_hub --algo ppo --env seals/Ant-v0 -f logs/ -orga HumanCompatibleAI
 ```
 
 ## Hyperparameters
@@ -60,11 +66,17 @@ OrderedDict([('batch_size', 16),
              ('n_epochs', 10),
              ('n_steps', 2048),
              ('n_timesteps', 1000000.0),
-             ('normalize', True),
+             ('normalize',
+              {'gamma': 0.995, 'norm_obs': False, 'norm_reward': True}),
              ('policy', 'MlpPolicy'),
              ('policy_kwargs',
-              'dict(activation_fn=nn.Tanh, net_arch=[dict(pi=[64, 64], vf=[64, '
-              '64])])'),
+              {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>,
+               'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
+               'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
              ('vf_coef', 0.4351450387648799),
-             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+             ('normalize_kwargs',
+              {'norm_obs': {'gamma': 0.995,
+                            'norm_obs': False,
+                            'norm_reward': True},
+               'norm_reward': False})])
 ```

args.yml

@@ -1,6 +1,8 @@
 !!python/object/apply:collections.OrderedDict
 - - - algo
     - ppo
+  - - conf_file
+    - hyperparams/python/ppo.py
   - - device
     - cpu
   - - env
@@ -16,7 +18,7 @@
   - - hyperparams
     - null
   - - log_folder
-    - seals_experts_wandb_oldpickle/seed_6/
+    - logs
   - - log_interval
     - -1
   - - max_total_trials
@@ -41,6 +43,8 @@
     - null
   - - optimize_hyperparameters
     - false
+  - - progress
+    - false
   - - pruner
     - median
   - - sampler
@@ -56,7 +60,7 @@
   - - study_name
     - null
   - - tensorboard_log
-    - runs/seals/Ant-v0__ppo__6__1658839810
+    - runs/seals/Ant-v0__ppo__6__1670517965
   - - track
     - true
   - - trained_agent
@@ -70,6 +74,8 @@
   - - verbose
     - 1
   - - wandb_entity
-    - null
+    - ernestum
   - - wandb_project_name
-    - seals-experts-oldpickle
+    - seals-experts-normalized
+  - - yaml_file
+    - null

config.yml

@@ -20,10 +20,20 @@
   - - n_timesteps
     - 1000000.0
   - - normalize
-    - true
+    - gamma: 0.995
+      norm_obs: false
+      norm_reward: true
   - - policy
     - MlpPolicy
   - - policy_kwargs
-    - dict(activation_fn=nn.Tanh, net_arch=[dict(pi=[64, 64], vf=[64, 64])])
+    - activation_fn: !!python/name:torch.nn.modules.activation.Tanh ''
+      features_extractor_class: !!python/name:imitation.policies.base.NormalizeFeaturesExtractor ''
+      net_arch:
+      - pi:
+        - 64
+        - 64
+        vf:
+        - 64
+        - 64
   - - vf_coef
     - 0.4351450387648799

ppo-seals-Ant-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4a7f4f3680a55594f769c006d119e18bc5986c0acda722f941ec55e2d714e7cc
-size 326656
+oid sha256:1a6b9a42330bc2c39b68b9f94073db60c819a5ff57b5b22c59a05733f9d66b71
+size 325290

ppo-seals-Ant-v0/_stable_baselines3_version

@@ -1 +1 @@
-1.6.0
+1.6.2

ppo-seals-Ant-v0/data

@@ -4,24 +4,24 @@
         ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
         "__module__": "stable_baselines3.common.policies",
         "__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    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fa1852e88b0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa1852e8940>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa1852e89d0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa1852e8a60>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fa1852e8af0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fa1852e8b80>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa1852e8c10>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fa1852e8ca0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa1852e8d30>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa1852e8dc0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa1852e8e50>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f3eaaac4700>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3eaaac4790>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3eaaac4820>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3eaaac48b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3eaaac4940>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3eaaac49d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3eaaac4a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3eaaac4af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3eaaac4b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3eaaac4c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3eaaac4ca0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fa1852dde70>"
+        "_abc_impl": "<_abc_data object at 0x7f3eaaabcbd0>"
     },
     "verbose": 1,
     "policy_kwargs": {
         ":type:": "<class 'dict'>",
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+        ":serialized:": "gAWVvAAAAAAAAAB9lCiMDWFjdGl2YXRpb25fZm6UjBt0b3JjaC5ubi5tb2R1bGVzLmFjdGl2YXRpb26UjARUYW5olJOUjAhuZXRfYXJjaJRdlH2UKIwCcGmUXZQoS0BLQGWMAnZmlF2UKEtAS0BldWGMGGZlYXR1cmVzX2V4dHJhY3Rvcl9jbGFzc5SMF2ltaXRhdGlvbi5wb2xpY2llcy5iYXNllIwaTm9ybWFsaXplRmVhdHVyZXNFeHRyYWN0b3KUk5R1Lg==",
         "activation_fn": "<class 'torch.nn.modules.activation.Tanh'>",
         "net_arch": [
             {
@@ -34,7 +34,8 @@
                     64
                 ]
             }
-        ]
+        ],
+        "features_extractor_class": "<class 'imitation.policies.base.NormalizeFeaturesExtractor'>"
     },
     "observation_space": {
         ":type:": "<class 'gym.spaces.box.Box'>",
@@ -51,7 +52,7 @@
     },
     "action_space": {
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     },
     "_last_obs": null,
     "_last_episode_starts": {
@@ -93,7 +94,7 @@
     "_current_progress_remaining": -0.0014719999999999178,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": 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