Initial commit

args.yml

@@ -16,7 +16,7 @@
   - - hyperparams
     - null
   - - log_folder
-    - seals_experts_wandb/
+    - seals_experts_wandb_oldpickle/seed_9/
   - - log_interval
     - -1
   - - max_total_trials
@@ -56,7 +56,7 @@
   - - study_name
     - null
   - - tensorboard_log
-    - runs/seals/Swimmer-v0__ppo__9__1658503223
+    - runs/seals/Swimmer-v0__ppo__9__1658842767
   - - track
     - true
   - - trained_agent
@@ -72,4 +72,4 @@
   - - wandb_entity
     - null
   - - wandb_project_name
-    - seals-experts
+    - seals-experts-oldpickle

ppo-seals-Swimmer-v0.zip

@@ -1,3 +1,3 @@
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+oid sha256:ce3b4f1698e5a40488c4f0e5fdeb1881b452ef9dc94608577be4b06ea648be55
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ppo-seals-Swimmer-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 0x7f9c471dc790>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9c471dc820>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9c471dc8b0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9c471dc940>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f9c471dc9d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f9c471dca60>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9c471dcaf0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f9c471dcb80>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9c471dcc10>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9c471dcca0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9c471dcd30>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f0329fb18b0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0329fb1940>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0329fb19d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0329fb1a60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f0329fb1af0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f0329fb1b80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0329fb1c10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f0329fb1ca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0329fb1d30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0329fb1dc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0329fb1e50>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f9c471d3cc0>"
+        "_abc_impl": "<_abc_data object at 0x7f0329fa5e70>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -68,15 +68,15 @@
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
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     },
-    "tensorboard_log": "runs/seals/Swimmer-v0__ppo__9__1658503223/seals-Swimmer-v0",
+    "tensorboard_log": "runs/seals/Swimmer-v0__ppo__9__1658842767/seals-Swimmer-v0",
     "lr_schedule": {
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     },
     "_last_obs": null,
     "_last_episode_starts": {
@@ -93,7 +93,7 @@
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         ":type:": "<class 'collections.deque'>",
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"
+        ":serialized:": 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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -110,7 +110,7 @@
     "n_epochs": 20,
     "clip_range": {
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results.json

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train_eval_metrics.zip

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