PPO Lunar lander model

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 166.55 +/- 113.12
+      value: 277.10 +/- 19.33
       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 0x000001CD3B6E2D30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001CD3B6E2DC0>", "reset_noise": 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lunarlander-ppo.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0bd7acc035acb617cfa664761f37ae9ab9c4a3e0f256a70843f246c25db04089
-size 147259
+oid sha256:90ca1805b01086cf0e5d28aaa266e1db991c3061209e435c2c63c31aa2d1e8e3
+size 147135

lunarlander-ppo/data

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         ":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). 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 0x000001CD3B6E2D30>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001CD3B6E2DC0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001CD3B6E2E50>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001CD3B6E2EE0>",
-        "_build": "<function ActorCriticPolicy._build at 0x000001CD3B6E2F70>",
-        "forward": "<function ActorCriticPolicy.forward at 0x000001CD3B6E4040>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x000001CD3B6E40D0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001CD3B6E4160>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x000001CD3B6E41F0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001CD3B6E4280>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001CD3B6E4310>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x000001CD3B6E43A0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x000002359660AD30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000002359660ADC0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000002359660AE50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000002359660AEE0>",
+        "_build": "<function ActorCriticPolicy._build at 0x000002359660AF70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x000002359660D040>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x000002359660D0D0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000002359660D160>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x000002359660D1F0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000002359660D280>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000002359660D310>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x000002359660D3A0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x000001CD3B6E37C0>"
+        "_abc_impl": "<_abc._abc_data object at 0x000002359660C940>"
     },
     "verbose": 1,
     "policy_kwargs": {},
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     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1681580655523682200,
-    "learning_rate": 0.0003,
+    "start_time": 1681650256281218600,
+    "learning_rate": 0.001,
     "tensorboard_log": null,
     "lr_schedule": {
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     "ep_success_buffer": {
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@@ -79,10 +79,10 @@
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     "n_envs": 16,
     "n_steps": 1024,
-    "gamma": 0.999,
-    "gae_lambda": 0.95,
-    "ent_coef": 0.01,
-    "vf_coef": 0.5,
+    "gamma": 0.996,
+    "gae_lambda": 0.97,
+    "ent_coef": 0.001,
+    "vf_coef": 0.9,
     "max_grad_norm": 0.5,
     "batch_size": 64,
     "n_epochs": 4,

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 size 87929

lunarlander-ppo/policy.pth

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 size 43329

results.json

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-{"mean_reward": 166.54574228527605, "std_reward": 113.11761816062726, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-15T19:01:15.196921"}
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