First attempt at uploading to Huggingface

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 294.41 +/- 17.77
+      value: 298.53 +/- 18.94
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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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 0x7f55c871f4c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f55c871f560>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f55c871f600>", "_build_mlp_extractor": "<function 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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. 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results.json

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test.zip

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+oid sha256:e232ec1d472792c69fa8ea90eee4fbd4268ff920af6cab9d448804d4dd7f78a4
+size 146234

test/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 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 0x7f55c871f4c0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f55c871f560>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f55c871f600>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f55c871f6a0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f55c871f740>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f55c871f7e0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f55c871f880>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f55c871f920>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f55c871f9c0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f55c871fa60>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f55c871fb00>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7faa8292b4c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7faa8292b560>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7faa8292b600>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7faa8292b6a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7faa8292b740>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7faa8292b7e0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7faa8292b880>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7faa8292b920>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7faa8292b9c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7faa8292ba60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7faa8292bb00>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f55c9115bc0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7faa829267c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -41,13 +41,13 @@
         "dtype": "int64",
         "_np_random": null
     },
-    "n_envs": 6,
-    "num_timesteps": 100005888,
-    "_total_timesteps": 100000000,
+    "n_envs": 128,
+    "num_timesteps": 10092544,
+    "_total_timesteps": 10000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1672855078.3880582,
+    "start_time": 1672958649.6591418,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -57,30 +57,30 @@
     "_last_obs": null,
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
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     },
     "_last_original_obs": null,
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -5.888000000009441e-05,
+    "_current_progress_remaining": -0.009254400000000107,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
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