Upload model to Hugging Face

PPO-default.zip

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PPO-default/data

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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 0x7fce9cf353f0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fce9cf35480>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fce9cf35510>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fce9cf355a0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fce9cf35630>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fce9cf356c0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fce9cf35750>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fce9cf357e0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fce9cf35870>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fce9cf35900>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fce9cf35990>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fce9cf35a20>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fc47ce053f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc47ce05480>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc47ce05510>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc47ce055a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fc47ce05630>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fc47ce056c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc47ce05750>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc47ce057e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fc47ce05870>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc47ce05900>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc47ce05990>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc47ce05a20>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fce9cf31ac0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7fc47ce01b00>"
     },
     "verbose": true,
     "policy_kwargs": {},
@@ -48,7 +48,7 @@
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     "seed": null,
     "action_noise": null,
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+    "start_time": 1675310450205312370,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -57,7 +57,7 @@
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PPO-default/policy.optimizer.pth

@@ -1,3 +1,3 @@
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-oid sha256:83adb306f04a300a456711aea47c048d28cd05b552ab06807d86127335303350
+oid sha256:2bf2de655a33791b8a75e93202104735f0b6a53a562c439408fb8b93aa1a05f0
 size 82809

PPO-default/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:11d16ade94e051591d7ba9804e0e6c5e6406848085d49be27079395bc24583e9
+oid sha256:89702bed8ac6691f6892eab18f6870397a39032c90142fc4b27d1d940237c93e
 size 40833

README.md

@@ -16,7 +16,7 @@ model-index:
       type: Roomba
     metrics:
     - type: mean_reward
-      value: 215.78 +/- 264.27
+      value: 97.60 +/- 128.05
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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It allows to keep variance\n        above zero and prevent it from growing too fast. 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