PPO with 3e6 iterations

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 276.68 +/- 23.99
+      value: 281.43 +/- 23.89
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7fee74909ea0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fee74909f28>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fee7490f048>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fee7490f0d0>", "_build": "<function ActorCriticPolicy._build at 0x7fee7490f158>", "forward": "<function ActorCriticPolicy.forward at 0x7fee7490f1e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fee7490f268>", "_predict": "<function ActorCriticPolicy._predict at 0x7fee7490f2f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fee7490f378>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fee7490f400>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fee7490f488>", "__abstractmethods__": "frozenset()", "_abc_registry": "<_weakrefset.WeakSet object at 0x7fee74908240>", "_abc_cache": "<_weakrefset.WeakSet object at 0x7fee74908278>", "_abc_negative_cache": "<_weakrefset.WeakSet object at 0x7fee749082b0>", "_abc_negative_cache_version": 59}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": 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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 0x7f05a9379d08>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f05a9379d90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f05a9379e18>", "_build_mlp_extractor": "<function 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galeos_model_lander_ppo.zip

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-oid sha256:dfd48d7a45f20c0359da80e1b47acb8d2b549481cbb45ac6a5a74c061bf658ad
+oid sha256:739882ef90a54cbfb528c27235ed9b951c13d139788795a4f8af4b6c03b4d88e
 size 151163

galeos_model_lander_ppo/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    ",
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-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fee7490f400>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f05a9379e18>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f05a9379ea0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f05a9379f28>",
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+        "_predict": "<function ActorCriticPolicy._predict at 0x7f05a937e158>",
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+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f05a937e268>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f05a937e2f0>",
         "__abstractmethods__": "frozenset()",
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-        "_abc_negative_cache": "<_weakrefset.WeakSet object at 0x7fee749082b0>",
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         "_abc_negative_cache_version": 59
     },
     "verbose": 1,

results.json

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-{"mean_reward": 276.6844665838818, "std_reward": 23.994863457176383, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-29T21:25:44.457497"}
+{"mean_reward": 281.43437720458627, "std_reward": 23.892504554624935, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-29T21:31:39.071226"}