Initial commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 3261.29 +/- 1422.26
+      value: 2682.42 +/- 265.45
       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_oldpickle/seed_3/
   - - 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
-    - 1108849555
+    - 3
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - ''
-  - - total_n_trials
-    - null
+    - runs/seals/HalfCheetah-v0__ppo__3__1658844020
   - - track
-    - false
+    - true
   - - trained_agent
     - ''
   - - truncate_last_trajectory
@@ -72,4 +72,4 @@
   - - wandb_entity
     - null
   - - wandb_project_name
-    - sb3
+    - seals-experts-oldpickle

ppo-seals-HalfCheetah-v0.zip

@@ -1,3 +1,3 @@
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-size 167172
+oid sha256:0d84b4f1d99e0ddcfe9a04712e134aa6d30ea3a8474f349334fce36f326f6342
+size 170249

ppo-seals-HalfCheetah-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 0x7ff4150f1790>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff4150f1820>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff4150f18b0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff4150f1940>",
-        "_build": "<function ActorCriticPolicy._build at 0x7ff4150f19d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7ff4150f1a60>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff4150f1af0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7ff4150f1b80>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff4150f1c10>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff4150f1ca0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff4150f1d30>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f3998e418b0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3998e41940>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3998e419d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3998e41a60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3998e41af0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3998e41b80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3998e41c10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3998e41ca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3998e41d30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3998e41dc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3998e41e50>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7ff4150e7cc0>"
+        "_abc_impl": "<_abc_data object at 0x7f3998e36e70>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -68,15 +68,15 @@
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     "seed": 0,
     "action_noise": null,
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     "learning_rate": {
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-    "tensorboard_log": null,
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     "lr_schedule": {
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@@ -85,7 +85,7 @@
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