Trained PPO Lunar-Lander-v2

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 244.81 +/- 55.63
+      value: 217.25 +/- 70.45
       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 0x78f940517010>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78f9405170a0>", "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 0x78f940517010>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78f9405170a0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78f940517130>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78f9405171c0>",
-        "_build": "<function ActorCriticPolicy._build at 0x78f940517250>",
-        "forward": "<function ActorCriticPolicy.forward at 0x78f9405172e0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x78f940517370>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78f940517400>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x78f940517490>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78f940517520>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78f9405175b0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x78f940517640>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x152a72950>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x152a729e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x152a72a70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x152a72b00>",
+        "_build": "<function ActorCriticPolicy._build at 0x152a72b90>",
+        "forward": "<function ActorCriticPolicy.forward at 0x152a72c20>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x152a72cb0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x152a72d40>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x152a72dd0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x152a72e60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x152a72ef0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x152a72f80>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x78f9404b1700>"
+        "_abc_impl": "<_abc._abc_data object at 0x152a78fc0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -26,12 +26,12 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1712564150919914872,
+    "start_time": 1712946417637551000,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -45,13 +45,28 @@
     "_stats_window_size": 100,
     "ep_info_buffer": {
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+    "gamma": 0.99,
+    "gae_lambda": 0.95,
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+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
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+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
     "observation_space": {
         ":type:": "<class 'gymnasium.spaces.box.Box'>",
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@@ -69,7 +84,7 @@
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     "action_space": {
         ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
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         "n": "4",
         "start": "0",
         "_shape": [],
@@ -77,23 +92,8 @@
         "_np_random": null
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-    "n_steps": 2048,
-    "gamma": 0.99,
-    "gae_lambda": 0.95,
-    "ent_coef": 0.0,
-    "vf_coef": 0.5,
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-- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
-- Python: 3.10.12
+- OS: macOS-14.1.1-arm64-arm-64bit Darwin Kernel Version 23.1.0: Mon Oct  9 21:27:24 PDT 2023; root:xnu-10002.41.9~6/RELEASE_ARM64_T6000
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-- Cloudpickle: 2.2.1
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