Upload model to Hugging Face

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

PPO-default.zip

@@ -1,3 +1,3 @@
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+oid sha256:48a1505c6795a76355c83835deea6d14747b690eea958d3e08a8a760c9429151
 size 142406

PPO-default/data

@@ -4,20 +4,20 @@
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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 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 0x7f77d73d8c10>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f77d73d8ca0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f77d73d8d30>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f77d73d8dc0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f77d73d8e50>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f77d73d8ee0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f77d73d8f70>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f77d73d9000>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f77d73d9090>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f77d73d9120>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f77d73d91b0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f77d73d9240>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f2f3b5d4c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2f3b5d4ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2f3b5d4d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2f3b5d4dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f2f3b5d4e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f2f3b5d4ee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2f3b5d4f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2f3b5d5000>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f2f3b5d5090>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2f3b5d5120>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2f3b5d51b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2f3b5d5240>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f77d73cf800>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f2f3b5cf280>"
     },
     "verbose": true,
     "policy_kwargs": {},

README.md

@@ -16,7 +16,7 @@ model-index:
       type: RoombaAToB
     metrics:
     - type: mean_reward
-      value: -77.43 +/- 39.16
+      value: -13.40 +/- 47.78
       name: mean_reward
       verified: false
 ---

config.json

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results.json

@@ -1 +1 @@
-{"mean_reward": -77.4299756, "std_reward": 39.15808347387722, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T18:28:24.184327"}
+{"mean_reward": -13.4022832, "std_reward": 47.7808205467981, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T19:24:54.034446"}