Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: MiniGrid-FourRooms-v0
     metrics:
     - type: mean_reward
-      value: 0.00 +/- 0.00
+      value: 0.37 +/- 0.38
       name: mean_reward
       verified: false
 ---
@@ -65,6 +65,7 @@ python -m rl_zoo3.push_to_hub --algo ppo --env MiniGrid-FourRooms-v0 -f logs/ -o
 OrderedDict([('batch_size', 64),
              ('clip_range', 0.2),
              ('ent_coef', 0.0),
+             ('env_wrapper', 'gym_minigrid.wrappers.FlatObsWrapper'),
              ('gae_lambda', 0.95),
              ('gamma', 0.99),
              ('learning_rate', 0.00025),
@@ -73,6 +74,6 @@ OrderedDict([('batch_size', 64),
              ('n_steps', 512),
              ('n_timesteps', 4000000.0),
              ('normalize', True),
-             ('policy', 'MultiInputPolicy'),
+             ('policy', 'MlpPolicy'),
              ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
 ```

args.yml

@@ -10,7 +10,7 @@
   - - env_kwargs
     - null
   - - eval_episodes
-    - 5
+    - 20
   - - eval_freq
     - 25000
   - - gym_packages
@@ -24,7 +24,7 @@
   - - max_total_trials
     - null
   - - n_eval_envs
-    - 1
+    - 5
   - - n_evaluations
     - null
   - - n_jobs
@@ -32,7 +32,7 @@
   - - n_startup_trials
     - 10
   - - n_timesteps
-    - 10000
+    - -1
   - - n_trials
     - 500
   - - no_optim_plots
@@ -54,15 +54,15 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 2591642725
+    - 398822051
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - ''
+    - runs/MiniGrid-FourRooms-v0__ppo__398822051__1675792034
   - - track
-    - false
+    - true
   - - trained_agent
     - ''
   - - truncate_last_trajectory
@@ -74,7 +74,7 @@
   - - verbose
     - 1
   - - wandb_entity
-    - null
+    - openrlbenchmark
   - - wandb_project_name
     - sb3
   - - wandb_tags

config.yml

@@ -5,6 +5,8 @@
     - 0.2
   - - ent_coef
     - 0.0
+  - - env_wrapper
+    - gym_minigrid.wrappers.FlatObsWrapper
   - - gae_lambda
     - 0.95
   - - gamma
@@ -22,4 +24,4 @@
   - - normalize
     - true
   - - policy
-    - MultiInputPolicy
+    - MlpPolicy

ppo-MiniGrid-FourRooms-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:211ebccfeffd7b0b8acd1e40f5c322afb9a1f8f960c70ef05a7f1a7e7581a52f
-size 372691
+oid sha256:5f0754aaccf50d3a46911d8c98662f38ac1cf3acd072e6bcc525e660fdf292af
+size 4390722

ppo-MiniGrid-FourRooms-v0/data

@@ -1,21 +1,37 @@
 {
     "policy_class": {
         ":type:": "<class 'abc.ABCMeta'>",
-        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
         "__module__": "stable_baselines3.common.policies",
-        "__doc__": "\n    MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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    ",
-        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7fbf40ece160>",
+        "__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    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f9484e11e50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9484e11ee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9484e11f70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9484e14040>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f9484e140d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f9484e14160>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9484e141f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9484e14280>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f9484e14310>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9484e143a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9484e14430>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9484e144c0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fbf40ecf100>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f9484e12ec0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
     "observation_space": {
-        ":type:": "<class 'gym.spaces.dict.Dict'>",
-        ":serialized:": 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+        "dtype": "uint8",
+        "_shape": [
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+        ],
+        "low": "[0 0 0 ... 0 0 0]",
+        "high": "[255 255 255 ... 255 255 255]",
+        "bounded_below": "[ True  True  True ...  True  True  True]",
+        "bounded_above": "[ True  True  True ...  True  True  True]",
         "_np_random": null
     },
     "action_space": {
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         "_np_random": "RandomState(MT19937)"
     },
     "n_envs": 1,
-    "num_timesteps": 12288,
-    "_total_timesteps": 10000,
+    "num_timesteps": 4001792,
+    "_total_timesteps": 4000000,
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
-    "start_time": 1675779328967803661,
+    "start_time": 1675792036920533096,
     "learning_rate": {
         ":type:": "<class 'function'>",
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     },
-    "tensorboard_log": null,
+    "tensorboard_log": "runs/MiniGrid-FourRooms-v0__ppo__398822051__1675792034/MiniGrid-FourRooms-v0",
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