Better model performance

README.md

@@ -16,7 +16,7 @@ model-index:
       type: Swimmer-v3
     metrics:
     - type: mean_reward
-      value: 334.43 +/- 2.11
+      value: 366.72 +/- 0.68
       name: mean_reward
       verified: false
 ---

args.yml

@@ -10,7 +10,7 @@
   - - env_kwargs
     - null
   - - eval_episodes
-    - 5
+    - 20
   - - eval_freq
     - 25000
   - - gym_packages
@@ -18,13 +18,13 @@
   - - hyperparams
     - null
   - - log_folder
-    - logs/
+    - logs
   - - log_interval
     - -1
   - - max_total_trials
     - null
   - - n_eval_envs
-    - 1
+    - 5
   - - n_evaluations
     - null
   - - n_jobs
@@ -54,15 +54,15 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 2355280636
+    - 3324951722
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - ''
+    - runs/Swimmer-v3__ppo__3324951722__1673946687
   - - track
-    - false
+    - true
   - - trained_agent
     - ''
   - - truncate_last_trajectory
@@ -74,7 +74,7 @@
   - - verbose
     - 1
   - - wandb_entity
-    - null
+    - croumegous
   - - wandb_project_name
     - sb3
   - - wandb_tags

ppo-Swimmer-v3.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ef047b7f92d64cf54ebcdca780950bedb06929c52e9efb766afc2f23f04de9c8
-size 151697
+oid sha256:8ddeb385d47f4b117cc084e52fb50236603f5f7ff926f779e8c62bafc2b97664
+size 151380

ppo-Swimmer-v3/data

@@ -4,20 +4,20 @@
         ":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 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 0x7f92c4f3d670>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f92c4f3d700>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f92c4f3d790>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f92c4f3d820>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f92c4f3d8b0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f92c4f3d940>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f92c4f3d9d0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f92c4f3da60>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f92c4f3daf0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f92c4f3db80>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f92c4f3dc10>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f92c4f3dca0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f2e940d0a60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2e940d0af0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2e940d0b80>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2e940d0c10>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f2e940d0ca0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f2e940d0d30>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2e940d0dc0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2e940d0e50>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f2e940d0ee0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2e940d0f70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2e940d6040>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2e940d60d0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f92c4f3f090>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f2e9442bec0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -36,7 +36,7 @@
     },
     "action_space": {
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@@ -70,7 +70,7 @@
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@@ -78,7 +78,7 @@
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-- Python: 3.8.10
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