Initial commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 970.83 +/- 395.96
+      value: 1185.21 +/- 557.92
       name: mean_reward
     task:
       type: reinforcement-learning

args.yml

@@ -12,13 +12,15 @@
   - - eval_freq
     - 25000
   - - gym_packages
-    - []
+    - - seals
   - - hyperparams
     - null
   - - log_folder
-    - seals_experts
+    - seals_experts_wandb/
   - - log_interval
     - -1
+  - - max_total_trials
+    - null
   - - n_eval_envs
     - 1
   - - n_evaluations
@@ -34,7 +36,7 @@
   - - no_optim_plots
     - false
   - - num_threads
-    - 4
+    - 1
   - - optimization_log_path
     - null
   - - optimize_hyperparameters
@@ -48,17 +50,15 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 971226628
+    - 6
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - ''
-  - - total_n_trials
-    - null
+    - runs/seals/Ant-v0__ppo__6__1658498382
   - - track
-    - false
+    - true
   - - trained_agent
     - ''
   - - truncate_last_trajectory
@@ -72,4 +72,4 @@
   - - wandb_entity
     - null
   - - wandb_project_name
-    - sb3
+    - seals-experts

ppo-seals-Ant-v0.zip

@@ -1,3 +1,3 @@
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+oid sha256:3c723727fab9049fd9ac01ceed0b99183feda484707a860b6964ddbedcea77d3
+size 323114

ppo-seals-Ant-v0/_stable_baselines3_version

@@ -1 +1 @@
-1.5.1a8
+1.6.0

ppo-seals-Ant-v0/data

@@ -4,19 +4,19 @@
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         "__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 0x7ff36c575670>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff36c575700>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff36c575790>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff36c575820>",
-        "_build": "<function ActorCriticPolicy._build at 0x7ff36c5758b0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7ff36c575940>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff36c5759d0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7ff36c575a60>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff36c575af0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff36c575b80>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff36c575c10>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f820a07d790>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f820a07d820>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f820a07d8b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f820a07d940>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f820a07d9d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f820a07da60>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f820a07daf0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f820a07db80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f820a07dc10>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f820a07dca0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f820a07dd30>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7ff36c571240>"
+        "_abc_impl": "<_abc_data object at 0x7f820a074cc0>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -68,15 +68,15 @@
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     "seed": 0,
     "action_noise": null,
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"
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"
     },
     "ep_success_buffer": {
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