Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 248.05 +/- 44.30
+      value: 285.61 +/- 17.40
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":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 0x7f31a6918d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f31a6918dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f31a6918e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f31a6918ee0>", "_build": "<function ActorCriticPolicy._build at 0x7f31a6918f70>", "forward": "<function ActorCriticPolicy.forward at 0x7f31a691d040>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f31a691d0d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f31a691d160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f31a691d1f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f31a691d280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f31a691d310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f31a691d3a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f31a698ff90>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677055515783724709, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "n_steps": 1024, "gamma": 0.99, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":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 0x7fbbbc8454c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbbbc845550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbbbc8455e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbbbc845670>", "_build": "<function ActorCriticPolicy._build at 0x7fbbbc845700>", "forward": "<function ActorCriticPolicy.forward at 0x7fbbbc845790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbbbc845820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbbbc8458b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbbbc845940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbbbc8459d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbbbc845a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbbbc845af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fbbbc844240>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677061450815972910, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.007616000000000067, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 492, "n_steps": 1024, "gamma": 0.99, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ead8b69fd5cf786f57b3b5d661ea4da74c9f47c6bd895b72aca4468c3cec5055
-size 147335
+oid sha256:838e665f5094c65bd0ea322327bde17c9839718007c7349f46e5586e8a530e8a
+size 146780

ppo-LunarLander-v2/data

@@ -4,20 +4,20 @@
         ":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 0x7f31a6918d30>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f31a6918dc0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f31a6918e50>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f31a6918ee0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f31a6918f70>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f31a691d040>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f31a691d0d0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f31a691d160>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f31a691d1f0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f31a691d280>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f31a691d310>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f31a691d3a0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fbbbc8454c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbbbc845550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbbbc8455e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbbbc845670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbbbc845700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbbbc845790>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbbbc845820>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbbbc8458b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbbbc845940>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbbbc8459d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbbbc845a60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbbbc845af0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f31a698ff90>"
+        "_abc_impl": "<_abc_data object at 0x7fbbbc844240>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -43,12 +43,12 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 1015808,
-    "_total_timesteps": 1000000,
+    "num_timesteps": 2015232,
+    "_total_timesteps": 2000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1677055515783724709,
+    "start_time": 1677061450815972910,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -57,7 +57,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAPOViL3E26U/wKP0vpe/Db8g7Ee9XYdNvgAAAAAAAAAAs6tDPX6mgT5N4pC+tzm8vjOxiL1NW9S9AAAAAAAAAAAz9Km9hayGu56KaD5KpAM9SEbTvAhp3D0AAAAAAAAAAMCjmD2uP5u6lpi6tnGBq7HNEg2721DYNQAAgD8AAIA/M4UkvZ8//DwTkw4+K+iKvmCwtz1x0Kk9AAAAAAAAAABmpsY5PdOfPwJcMzuKPzC/TbLduUphILoAAAAAAAAAAACMwDyyybg/su1HP8zH6j67+6i8ipnJvQAAAAAAAAAAvcqhvkXwpz4OJq8+32sRv6sGs77qs4Q+AAAAAAAAAACAP3A+ulknP7MIDL4Fcf6+Un8PPsif270AAAAAAAAAACChOL5pEGy8kqRBu0uok7m0oNg9enFuOgAAgD8AAIA/AJNDPRTwobquhg67Meq/tbYAWTmiQiw1AAAAAAAAAACaaTK+LxM4P/6akjxcQBG/vphgvk42Ij0AAAAAAAAAAABEATxxE2C7usW1OyEnjjwSC6g81g90vQAAgD8AAIA/zY76PM9WMz0XsyW+tPFjvmej6bz/YRI7AAAAAAAAAABmAh49j5Jruk69/Lqkyv82ApRyu1p8WbYAAAAAAAAAABsLlb4xIjk+6jjcPsnkn75Dk0Y9aP7RPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -67,16 +67,16 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.015808000000000044,
+    "_current_progress_remaining": -0.007616000000000067,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "gAWVPhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIr84xIPuIcUCUhpRSlIwBbJRL84wBdJRHQJNhl5v99+h1fZQoaAZoCWgPQwi3tBoSt1dwQJSGlFKUaBVL82gWR0CTY5xbB42TdX2UKGgGaAloD0MI02pI3KOwcUCUhpRSlGgVS91oFkdAk2OsHWz4UXV9lChoBmgJaA9DCBubHan+OXNAlIaUUpRoFU0IAWgWR0CTZIzWf9P2dX2UKGgGaAloD0MIbqMBvAWkckCUhpRSlGgVS/poFkdAk2THanJkoXV9lChoBmgJaA9DCLSvPEiPt3NAlIaUUpRoFUvVaBZHQJNlYJ2MbWF1fZQoaAZoCWgPQwjb+BOVDTByQJSGlFKUaBVL1WgWR0CTZdPxhDw6dX2UKGgGaAloD0MIZVHYRdFzcECUhpRSlGgVS/5oFkdAk2Y/EOy3TnV9lChoBmgJaA9DCAjnU8dq4nBAlIaUUpRoFU0CAWgWR0CTZtrZJ04jdX2UKGgGaAloD0MIbY/ecB+PbkCUhpRSlGgVS99oFkdAk2cKIN3GGXV9lChoBmgJaA9DCLHeqBUm1HBAlIaUUpRoFUvpaBZHQJNoJyXD3uh1fZQoaAZoCWgPQwhu3GJ+rntwQJSGlFKUaBVL22gWR0CTaOc8TzundX2UKGgGaAloD0MI7pi6K3shcUCUhpRSlGgVS+9oFkdAk2lser+5v3V9lChoBmgJaA9DCBCv6xcs+nFAlIaUUpRoFU0LAWgWR0CTavHf/FR6dX2UKGgGaAloD0MIeQPMfAePcUCUhpRSlGgVS99oFkdAk2yCKR+z+nV9lChoBmgJaA9DCBY1mIbhGHJAlIaUUpRoFUv1aBZHQJNtY7YChex1fZQoaAZoCWgPQwjHSPYIdbByQJSGlFKUaBVL5mgWR0CTbiS88La3dX2UKGgGaAloD0MI5SZqae4CcUCUhpRSlGgVS/xoFkdAk2736yjYZnV9lChoBmgJaA9DCOV9HM1R4nFAlIaUUpRoFUvqaBZHQJNvCwJPZZl1fZQoaAZoCWgPQwhkd4GSgtJyQJSGlFKUaBVLzWgWR0CTb2rlvIfbdX2UKGgGaAloD0MIMUW5ND6ecECUhpRSlGgVTQgBaBZHQJNxgiHIp6R1fZQoaAZoCWgPQwhxHk5gOlVwQJSGlFKUaBVL+WgWR0CTccoUi6g/dX2UKGgGaAloD0MIZyjueBOeb0CUhpRSlGgVS+poFkdAk3JsANoak3V9lChoBmgJaA9DCJ+RCI1g2G1AlIaUUpRoFUvsaBZHQJN0Gqo60Y11fZQoaAZoCWgPQwghPrDj/7JyQJSGlFKUaBVNSwFoFkdAk3QsxsVLz3V9lChoBmgJaA9DCLcLzXWaY29AlIaUUpRoFUvYaBZHQJN0+ur6tT11fZQoaAZoCWgPQwinPpC8c31yQJSGlFKUaBVNEAFoFkdAk3Ufr8iwCHV9lChoBmgJaA9DCCPcZFSZBG1AlIaUUpRoFUvxaBZHQJN32UbDMvB1fZQoaAZoCWgPQwgTntDrz/ZsQJSGlFKUaBVL6GgWR0CTeOVRk3CLdX2UKGgGaAloD0MIF56Xio3mcECUhpRSlGgVS+BoFkdAk3k4/A0sOHV9lChoBmgJaA9DCNUhN8MNqHFAlIaUUpRoFUvyaBZHQJN57WpZOi51fZQoaAZoCWgPQwiIEi15vOFvQJSGlFKUaBVL/WgWR0CTepHX2/SIdX2UKGgGaAloD0MIXCBB8eN4YUCUhpRSlGgVTegDaBZHQJN7V1xKg7J1fZQoaAZoCWgPQwgbguMyrmlyQJSGlFKUaBVLumgWR0CTe55DZ13ddX2UKGgGaAloD0MIAYkmUEQ6cUCUhpRSlGgVS/loFkdAk3vm3BpHqnV9lChoBmgJaA9DCBJpG38iOHFAlIaUUpRoFU0JAWgWR0CTfRje9Ba+dX2UKGgGaAloD0MIEsDN4gUocUCUhpRSlGgVS9loFkdAk31oTGo73nV9lChoBmgJaA9DCDxM++b+zVxAlIaUUpRoFU3oA2gWR0CTfYqdH2AYdX2UKGgGaAloD0MIyHxAoLOtc0CUhpRSlGgVTSsBaBZHQJN9wLhJiAl1fZQoaAZoCWgPQwhI+rSK/vhwQJSGlFKUaBVL/mgWR0CTfef2saKldX2UKGgGaAloD0MIW86luOoEcUCUhpRSlGgVS+5oFkdAk333gpBomHV9lChoBmgJaA9DCKrTgazne3BAlIaUUpRoFUviaBZHQJOS2MZP2wp1fZQoaAZoCWgPQwia7+AnjtZxQJSGlFKUaBVNDAFoFkdAk5MFMdtEX3V9lChoBmgJaA9DCA+4rpjRunBAlIaUUpRoFUv0aBZHQJOTHY150KZ1fZQoaAZoCWgPQwinCHB6F6duQJSGlFKUaBVL3GgWR0CTk0J7sv7FdX2UKGgGaAloD0MI1ZY6yCtScECUhpRSlGgVS+RoFkdAk5QESIxgzHV9lChoBmgJaA9DCJP+XgqPsXFAlIaUUpRoFUviaBZHQJOUob4rSVp1fZQoaAZoCWgPQwhkWwacpTRuQJSGlFKUaBVL2GgWR0CTlM9deIEbdX2UKGgGaAloD0MINV66SQwnXkCUhpRSlGgVTegDaBZHQJOVwouwost1fZQoaAZoCWgPQwj9oC5SKD1xQJSGlFKUaBVNBwFoFkdAk5Xegg5imXV9lChoBmgJaA9DCCR+xRouEkxAlIaUUpRoFUvJaBZHQJOWEChew9t1fZQoaAZoCWgPQwidK0oJQXdxQJSGlFKUaBVL32gWR0CTln1A7gbZdX2UKGgGaAloD0MIfJxpwjbbcUCUhpRSlGgVS/poFkdAk5cHLmp2lnV9lChoBmgJaA9DCPPMy2E3E3JAlIaUUpRoFU0LAWgWR0CTl1lNlAeJdX2UKGgGaAloD0MIfjuJCH8MckCUhpRSlGgVTRoBaBZHQJOXhfnfVI91fZQoaAZoCWgPQwjtn6cBA89uQJSGlFKUaBVNEQFoFkdAk5f1XmvGInV9lChoBmgJaA9DCMR6o1aYliZAlIaUUpRoFUu3aBZHQJOX9Bt1p0x1fZQoaAZoCWgPQwgNNnUeVZxwQJSGlFKUaBVL3GgWR0CTmPMPSUkfdX2UKGgGaAloD0MItdyZCYadb0CUhpRSlGgVS/JoFkdAk5lEPxx1gnV9lChoBmgJaA9DCL4uw3+6d0BAlIaUUpRoFUuyaBZHQJOZY0waisZ1fZQoaAZoCWgPQwiqfqXzoXpwQJSGlFKUaBVL/2gWR0CTmelruYx+dX2UKGgGaAloD0MIRnh7EII8cECUhpRSlGgVTQYBaBZHQJOaywFC9h91fZQoaAZoCWgPQwi/0vnwrF5tQJSGlFKUaBVL6mgWR0CTm6Ujs2NvdX2UKGgGaAloD0MI492RsdodbkCUhpRSlGgVS/BoFkdAk5woP9UCJXV9lChoBmgJaA9DCCfdlsgFnG5AlIaUUpRoFUvkaBZHQJOcSHVPN3Z1fZQoaAZoCWgPQwjqQUEpWplJQJSGlFKUaBVLxGgWR0CTnQUnG828dX2UKGgGaAloD0MI1eqrq0JacUCUhpRSlGgVTRcBaBZHQJOdDUNKAax1fZQoaAZoCWgPQwhhjbPpiGFxQJSGlFKUaBVL8WgWR0CTnTao/A0sdX2UKGgGaAloD0MIoDU//hJncUCUhpRSlGgVS/ZoFkdAk53YrSVnmXV9lChoBmgJaA9DCGrf3F+9dHBAlIaUUpRoFUv7aBZHQJOdzMEA5rB1fZQoaAZoCWgPQwjv4ZLjDsVxQJSGlFKUaBVNAwFoFkdAk56lAZ88cXV9lChoBmgJaA9DCA8PYfw0I2BAlIaUUpRoFU3oA2gWR0CToMttALRbdX2UKGgGaAloD0MIZHWr56SLcUCUhpRSlGgVTSYBaBZHQJOhDxLCemN1fZQoaAZoCWgPQwgyWHGqtWhGQJSGlFKUaBVLxGgWR0CToW3R5TqCdX2UKGgGaAloD0MIxHjNqzqrckCUhpRSlGgVTQ0BaBZHQJOhq7qY7aJ1fZQoaAZoCWgPQwifA8sRMkpxQJSGlFKUaBVL8mgWR0CTofqGUOd5dX2UKGgGaAloD0MIKNTTR6BicECUhpRSlGgVTUEBaBZHQJOivOD8Lrp1fZQoaAZoCWgPQwhgr7DgfpRuQJSGlFKUaBVL0WgWR0CTou/GVAzIdX2UKGgGaAloD0MIJSAm4QIFckCUhpRSlGgVTRkBaBZHQJOlsGLUCq91fZQoaAZoCWgPQwix3qgVpgRuQJSGlFKUaBVL6GgWR0CTpjDbJwKjdX2UKGgGaAloD0MIaOxLNl5FcUCUhpRSlGgVTQkBaBZHQJOmXF1jiGZ1fZQoaAZoCWgPQwh0Yg/tYyhyQJSGlFKUaBVNEgFoFkdAk6avUF0PpnV9lChoBmgJaA9DCESGVbxRPnBAlIaUUpRoFUvXaBZHQJOm0F0PpY91fZQoaAZoCWgPQwiCyY0iq/9xQJSGlFKUaBVNFAFoFkdAk6cHT/hl2HV9lChoBmgJaA9DCMbBpWPOOXBAlIaUUpRoFU0nAWgWR0CTqLgsbvPUdX2UKGgGaAloD0MIlx+4ypMwckCUhpRSlGgVTeABaBZHQJOpXWbwz+F1fZQoaAZoCWgPQwjzWDMyCPZwQJSGlFKUaBVL0WgWR0CTqjIi1RcedX2UKGgGaAloD0MIj+OHSqNVbUCUhpRSlGgVS+5oFkdAk6rUY8+zMXV9lChoBmgJaA9DCPnAjv/CMnFAlIaUUpRoFUv2aBZHQJOq7Hjp9ql1fZQoaAZoCWgPQwjqCUs8oOdxQJSGlFKUaBVL7GgWR0CTq6k4m1IAdX2UKGgGaAloD0MItOTxtDwMcUCUhpRSlGgVS+NoFkdAk6wIhQm/nHV9lChoBmgJaA9DCOLMr+YAynBAlIaUUpRoFUv6aBZHQJOtHc0tRN11fZQoaAZoCWgPQwh8KTxotvdwQJSGlFKUaBVLzGgWR0CTrfvv0AcUdX2UKGgGaAloD0MIPbg7a7cXcUCUhpRSlGgVS9JoFkdAk690fcN6PnV9lChoBmgJaA9DCNbJGYr7E3JAlIaUUpRoFUvsaBZHQJOv+iAUcn51fZQoaAZoCWgPQwioHJPFPRVyQJSGlFKUaBVLzWgWR0CTsXKeCkGidX2UKGgGaAloD0MITU2CNyQqbECUhpRSlGgVTQABaBZHQJOxb7O3UhF1fZQoaAZoCWgPQwgw9fOmIsJvQJSGlFKUaBVNBwFoFkdAk7ImnGbTdHV9lChoBmgJaA9DCOELk6kCfXFAlIaUUpRoFU00AWgWR0CTs3hCdBjXdX2UKGgGaAloD0MIi8VvCmt7ckCUhpRSlGgVTbsBaBZHQJO0NXU6PsB1ZS4="
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 248,
+    "_n_updates": 492,
     "n_steps": 1024,
     "gamma": 0.99,
     "gae_lambda": 0.98,

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e3389ee215193e93dd00d1fcef01cbe68186dfbfb43276c963825591dcab16c6
-size 87929
+oid sha256:e0b57744a67c1d76a7f759a791b60d10c91bb4d4c8218b6102f3ce4e159631e9
+size 87545

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7cfe9d16f8a3be87a5ee9f128fa5bf702e37b46e31f7bd8f1bf7e7b915c9fa0d
-size 43393
+oid sha256:9809fa977a932b6eeb974b021d7d8ed3dae6027c37a1b031aea25ac24fa7ad69
+size 43265

ppo-LunarLander-v2/system_info.txt

@@ -2,6 +2,6 @@
 - Python: 3.8.10
 - Stable-Baselines3: 1.7.0
 - PyTorch: 1.13.1+cu116
-- GPU Enabled: True
+- GPU Enabled: False
 - Numpy: 1.21.6
 - Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 248.04967279960783, "std_reward": 44.30206389354817, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-22T09:06:43.060633"}
+{"mean_reward": 285.6063563045128, "std_reward": 17.399000716367663, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-22T11:09:07.714100"}