test

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 253.82 +/- 62.97
+      value: 273.28 +/- 19.40
       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. 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 0x7fbe1f440790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbe1f440820>", "reset_noise": 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         "__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 0x7fbe1f440790>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbe1f440820>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbe1f4408b0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbe1f440940>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fbe1f4409d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fbe1f440a60>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbe1f440af0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbe1f440b80>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fbe1f440c10>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbe1f440ca0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbe1f440d30>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbe1f440dc0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f386fb17490>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f386fb17520>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f386fb175b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f386fb17640>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f386fb176d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f386fb17760>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f386fb177f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f386fb17880>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f386fb17910>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f386fb179a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f386fb17a30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f386fb17ac0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fbe1f43bb00>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f386fb198c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -27,12 +27,9 @@
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     "action_noise": null,
     "start_time": 1688798447877710999,
-    "learning_rate": 0.0003,
+    "learning_rate": 0.0,
     "tensorboard_log": null,
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-    },
+    "_last_obs": null,
     "_last_episode_starts": {
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@@ -76,7 +73,7 @@
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         "_np_random": null
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-    "n_envs": 20,
+    "n_envs": 30,
     "n_steps": 2048,
     "gamma": 0.99,
     "gae_lambda": 0.95,
@@ -87,13 +84,13 @@
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     "clip_range": {
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results.json

@@ -1 +1 @@
-{"mean_reward": 253.81581280000006, "std_reward": 62.97115008786558, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-08T08:04:21.785838"}
+{"mean_reward": 273.280263, "std_reward": 19.398150988744053, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-08T14:52:20.727614"}