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 0x7f94c4c35360>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f94c4c353f0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f94c4c35480>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f94c4c35510>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f94c4c355a0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f94c4c35630>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f94c4c356c0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f94c4c35750>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f94c4c357e0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f94c4c35870>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f94c4c35900>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f94c4c35990>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fcb0fb313f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcb0fb31480>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcb0fb31510>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcb0fb315a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fcb0fb31630>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fcb0fb316c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcb0fb31750>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcb0fb317e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fcb0fb31870>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcb0fb31900>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcb0fb31990>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcb0fb31a20>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f94c4c31540>"
+        "_abc_impl": "<_abc._abc_data object at 0x7fcb0fb2dbc0>"
     },
     "verbose": true,
     "policy_kwargs": {},
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     "seed": null,
     "action_noise": null,
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+    "start_time": 1675308159984252897,
     "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:ed41452c0e4f48bbdd64ac708e8263eb393651ec2ff6ebe27ef76c46ffb272e0
+oid sha256:9cda92f0a6a671f2d4d79468cec723d3542d7a49705e18c70f98ee681bdf72c1
 size 82809

PPO-default/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:df14efaab1908c15c4ad336d2fa13b58c9f7fe38d3d01db7bbf5957d9de66aa1
+oid sha256:5938290ce126c85242ee69ad78156ac67f52ecf933c31335d8b25c9b2be7f84f
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README.md

@@ -16,7 +16,7 @@ model-index:
       type: Roomba
     metrics:
     - type: mean_reward
-      value: 138.30 +/- 101.82
+      value: 45.60 +/- 108.13
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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). 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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 0x7f94c4c35360>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f94c4c353f0>", "reset_noise": 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