Upload PPO LunarLander-v2 trained agent with 2M steps

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 280.18 +/- 18.76
+      value: 255.93 +/- 75.33
       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 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 0x7f5df17e9e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5df17e9ee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5df17e9f70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5df17f0040>", "_build": "<function ActorCriticPolicy._build at 0x7f5df17f00d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f5df17f0160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5df17f01f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5df17f0280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5df17f0310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5df17f03a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5df17f0430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5df17ed0c0>"}, "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": 1670333515361515124, "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": 0.999, "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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "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 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 0x7f975ccf25e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f975ccf2670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f975ccf2700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f975ccf2790>", "_build": "<function ActorCriticPolicy._build at 0x7f975ccf2820>", "forward": "<function ActorCriticPolicy.forward at 0x7f975ccf28b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f975ccf2940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f975ccf29d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f975ccf2a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f975ccf2af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f975ccf2b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f975cce7ea0>"}, "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": 2506752, "_total_timesteps": 2500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670525885631455610, "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": -0.0027007999999999477, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 765, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 5, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "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:bb9b1bb07d99ec4344a78a99fa5d20dac9399ed554bf6bbd3b10186d5ddc8688
-size 147042
+oid sha256:350abf0f92b26c23b13ab89621dacdb91984f1061aae72e01d6ad48036ead1dd
+size 147107

ppo-LunarLander-v2/data

@@ -4,19 +4,19 @@
         ":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 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 0x7f5df17e9e50>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5df17e9ee0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5df17e9f70>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5df17f0040>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f5df17f00d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f5df17f0160>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5df17f01f0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f5df17f0280>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5df17f0310>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5df17f03a0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5df17f0430>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f975ccf25e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f975ccf2670>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f975ccf2700>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f975ccf2790>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f975ccf2820>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f975ccf28b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f975ccf2940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f975ccf29d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f975ccf2a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f975ccf2af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f975ccf2b80>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f5df17ed0c0>"
+        "_abc_impl": "<_abc_data object at 0x7f975cce7ea0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -42,12 +42,12 @@
         "_np_random": null
     },
     "n_envs": 16,
-    "num_timesteps": 2015232,
-    "_total_timesteps": 2000000,
+    "num_timesteps": 2506752,
+    "_total_timesteps": 2500000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1670333515361515124,
+    "start_time": 1670525885631455610,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -56,7 +56,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -66,16 +66,16 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.007616000000000067,
+    "_current_progress_remaining": -0.0027007999999999477,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "gAWVKxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIZqGd06wpcECUhpRSlIwBbJRL2YwBdJRHQJ4ZicRUWEd1fZQoaAZoCWgPQwgz4Zf6+UpxQJSGlFKUaBVL5GgWR0CeGfmJ3xFzdX2UKGgGaAloD0MIk6rtJvhQbkCUhpRSlGgVS+NoFkdAnhogaef7JnV9lChoBmgJaA9DCHJr0m1J7nFAlIaUUpRoFUv9aBZHQJ4aR1q33Ht1fZQoaAZoCWgPQwjfGAKAY9RvQJSGlFKUaBVL6GgWR0CeGnxvNu+AdX2UKGgGaAloD0MIca/MW3XRc0CUhpRSlGgVS99oFkdAnhsiaJAMUnV9lChoBmgJaA9DCGt+/KXFAnNAlIaUUpRoFU0wAWgWR0CeG6m78Nx3dX2UKGgGaAloD0MIYr68APvJcECUhpRSlGgVS8RoFkdAnhv7aRISUXV9lChoBmgJaA9DCFddh2pKMHFAlIaUUpRoFUvraBZHQJ4cnbi6xxF1fZQoaAZoCWgPQwhFY+3vLN9xQJSGlFKUaBVL32gWR0CeHLQqqfe2dX2UKGgGaAloD0MIqfqVzkeUckCUhpRSlGgVTQYBaBZHQJ4dk76pHZt1fZQoaAZoCWgPQwjA7J48LCpxQJSGlFKUaBVL92gWR0CeHonCwbEQdX2UKGgGaAloD0MID9Qpjy79ckCUhpRSlGgVS+9oFkdAnh6QTmGM43V9lChoBmgJaA9DCILK+PdZeHBAlIaUUpRoFUveaBZHQJ4fiIUJv5x1fZQoaAZoCWgPQwjIX1rUJ7ptQJSGlFKUaBVL8WgWR0CeH8qDbrTqdX2UKGgGaAloD0MIGAtD5LTnckCUhpRSlGgVS9doFkdAnh/Gpda+vnV9lChoBmgJaA9DCPQVpBkL8W9AlIaUUpRoFUvPaBZHQJ4gHIYFaB91fZQoaAZoCWgPQwj6CWe3lilxQJSGlFKUaBVL5mgWR0CeIFtnf2sadX2UKGgGaAloD0MIU5eMYyQic0CUhpRSlGgVS/poFkdAniEN+TeO43V9lChoBmgJaA9DCBsOSwO/7mZAlIaUUpRoFU3oA2gWR0CeIRzFMqSYdX2UKGgGaAloD0MIa32R0FYAcUCUhpRSlGgVS91oFkdAniEpUo8ZDXV9lChoBmgJaA9DCPEqa5si4XBAlIaUUpRoFUvqaBZHQJ4h7S7Xg+B1fZQoaAZoCWgPQwhsXtVZLXhxQJSGlFKUaBVL6mgWR0CeIisz2vjfdX2UKGgGaAloD0MIwM+4cGANc0CUhpRSlGgVS+ZoFkdAniKp8rqdH3V9lChoBmgJaA9DCJvKorDLyHFAlIaUUpRoFU0RAWgWR0CeI9sWweNldX2UKGgGaAloD0MItXBZhU1mcUCUhpRSlGgVS/loFkdAniQdJSR8t3V9lChoBmgJaA9DCEJg5dBic3JAlIaUUpRoFUvvaBZHQJ44yvwEyL11fZQoaAZoCWgPQwjgaTLj7QRzQJSGlFKUaBVL2WgWR0CeOSWcjJMhdX2UKGgGaAloD0MI4+DSMacsckCUhpRSlGgVS/5oFkdAnjks41gpjXV9lChoBmgJaA9DCOXTY1sGp3FAlIaUUpRoFUvWaBZHQJ45RbUwztV1fZQoaAZoCWgPQwiSzOodbn9uQJSGlFKUaBVLyGgWR0CeOiAJb+tKdX2UKGgGaAloD0MIZ2X7kDfuY0CUhpRSlGgVTegDaBZHQJ46QzDXOGF1fZQoaAZoCWgPQwiiQnVzcbxyQJSGlFKUaBVL1WgWR0CeOmXbdrO8dX2UKGgGaAloD0MIiJ6USQ2ib0CUhpRSlGgVS9loFkdAnjqV7laKUHV9lChoBmgJaA9DCKu0xTX+0XFAlIaUUpRoFU0PAWgWR0CeOq9mYjSodX2UKGgGaAloD0MIU14roXtRckCUhpRSlGgVTQEBaBZHQJ46zOC5Etx1fZQoaAZoCWgPQwiaQuc1ttVyQJSGlFKUaBVNDwFoFkdAnjrlLFn7HnV9lChoBmgJaA9DCCKoGr1aAXBAlIaUUpRoFUvPaBZHQJ47C2gFotd1fZQoaAZoCWgPQwgAcy1aQJhwQJSGlFKUaBVL5GgWR0CeO5wsXizcdX2UKGgGaAloD0MIVwqBXKKUckCUhpRSlGgVS9NoFkdAnjurn1WbPXV9lChoBmgJaA9DCC6u8ZnsMnBAlIaUUpRoFUvLaBZHQJ48o1xbSql1fZQoaAZoCWgPQwjGia92lBRyQJSGlFKUaBVL3mgWR0CePNwe/5+IdX2UKGgGaAloD0MIylGAKJjIcECUhpRSlGgVS9JoFkdAnj2x3aBZp3V9lChoBmgJaA9DCPMEwk4xbG9AlIaUUpRoFUvfaBZHQJ49sQGwA2h1fZQoaAZoCWgPQwjVWpiFNtxyQJSGlFKUaBVL3WgWR0CePfuOS4e+dX2UKGgGaAloD0MIAwgfSrQ7ckCUhpRSlGgVS9toFkdAnj4HE/B3zXV9lChoBmgJaA9DCJepSfAGQW1AlIaUUpRoFUvaaBZHQJ4+wsCkoF51fZQoaAZoCWgPQwheRxyyAYdtQJSGlFKUaBVLzmgWR0CePuytFKChdX2UKGgGaAloD0MIUwlP6LXIcUCUhpRSlGgVS79oFkdAnj7tVWCEpXV9lChoBmgJaA9DCHWvk/pyxnJAlIaUUpRoFUu8aBZHQJ4/Cl54W1t1fZQoaAZoCWgPQwj8VYDvdrhyQJSGlFKUaBVL2mgWR0CeP0oNd7fIdX2UKGgGaAloD0MISDZXzTOfcUCUhpRSlGgVS+9oFkdAnj9X6dlNDnV9lChoBmgJaA9DCOaQ1EJJpHJAlIaUUpRoFUvWaBZHQJ4/U0Nz8xd1fZQoaAZoCWgPQwgJbTmXYuhyQJSGlFKUaBVNAAFoFkdAnj/F10T103V9lChoBmgJaA9DCJlLqrbbWXBAlIaUUpRoFUvXaBZHQJ5APm1YyO91fZQoaAZoCWgPQwhu93KfnNJxQJSGlFKUaBVL3GgWR0CeQEdwNsnBdX2UKGgGaAloD0MICmR2Fr3+ckCUhpRSlGgVS79oFkdAnkC1B2OhkHV9lChoBmgJaA9DCOEmo8pwLXJAlIaUUpRoFUvtaBZHQJ5B5EWqLjx1fZQoaAZoCWgPQwhagoyASrpwQJSGlFKUaBVL3GgWR0CeQlXnQpnZdX2UKGgGaAloD0MINnUeFT/qcUCUhpRSlGgVS9JoFkdAnkJjZUT+N3V9lChoBmgJaA9DCIfcDDfgoHFAlIaUUpRoFUvdaBZHQJ5CsMqjJuF1fZQoaAZoCWgPQwigi4aMRyVzQJSGlFKUaBVL7GgWR0CeQrl+3H7xdX2UKGgGaAloD0MIvi8uVWnnbUCUhpRSlGgVS8VoFkdAnkLlPva11HV9lChoBmgJaA9DCEFn0qYqT3JAlIaUUpRoFUvOaBZHQJ5DVj0+TvB1fZQoaAZoCWgPQwhWt3pO+sdwQJSGlFKUaBVL7GgWR0CeQ/JOFg2IdX2UKGgGaAloD0MIWdsUj8s2cECUhpRSlGgVS8JoFkdAnkPxREWqLnV9lChoBmgJaA9DCIqw4emVmHFAlIaUUpRoFUvuaBZHQJ5EA01qFh51fZQoaAZoCWgPQwhHAaJgxh5uQJSGlFKUaBVL6GgWR0CeRE4AS39adX2UKGgGaAloD0MI4GWGjfL/cECUhpRSlGgVS+loFkdAnkROY+jdpXV9lChoBmgJaA9DCNEeL6QDiHNAlIaUUpRoFUvxaBZHQJ5Fineizs11fZQoaAZoCWgPQwgrL/mffO5yQJSGlFKUaBVNKgFoFkdAnkWxsVLzw3V9lChoBmgJaA9DCH1e8dQjD3BAlIaUUpRoFUv/aBZHQJ5F5vbXYlJ1fZQoaAZoCWgPQwjON6J71lRyQJSGlFKUaBVLu2gWR0CeRhqp97WvdX2UKGgGaAloD0MIjup0IGvMckCUhpRSlGgVTQsBaBZHQJ5Gmm1pj+d1fZQoaAZoCWgPQwhKJTyhF4ByQJSGlFKUaBVL02gWR0CeRwr6ciGGdX2UKGgGaAloD0MI0XgiiDO0c0CUhpRSlGgVS8doFkdAnkckUO/cnHV9lChoBmgJaA9DCAaAKm7cXHJAlIaUUpRoFUvmaBZHQJ5Hhdmg8KZ1fZQoaAZoCWgPQwhV3LjF/B1tQJSGlFKUaBVL3GgWR0CeR5Rk3CKrdX2UKGgGaAloD0MIB0SIK+cEckCUhpRSlGgVS89oFkdAnkfsAJb+tXV9lChoBmgJaA9DCOD3b16cgXJAlIaUUpRoFUu+aBZHQJ5IFPDYRNB1fZQoaAZoCWgPQwivsyH/jGhxQJSGlFKUaBVL1mgWR0CeSI3Ns3yadX2UKGgGaAloD0MIiiDOw4mYcUCUhpRSlGgVTQsBaBZHQJ5IvHvMKTl1fZQoaAZoCWgPQwiEuHL2ToZuQJSGlFKUaBVL22gWR0CeSLxG2CumdX2UKGgGaAloD0MI6PaSxihnckCUhpRSlGgVS9poFkdAnkj4CdSVGHV9lChoBmgJaA9DCErSNZNvTXJAlIaUUpRoFU0EAWgWR0CeSc7aZhKEdX2UKGgGaAloD0MIiIOEKN/McUCUhpRSlGgVS8hoFkdAnkoSpm29c3V9lChoBmgJaA9DCLN78rCQKnBAlIaUUpRoFUvYaBZHQJ5KFPtUn5V1fZQoaAZoCWgPQwh01NFxtWVuQJSGlFKUaBVL1GgWR0CeSiQ3gk1NdX2UKGgGaAloD0MIhXr6CLyJcUCUhpRSlGgVS9NoFkdAnkp2M0gr6XV9lChoBmgJaA9DCHLg1XIna3NAlIaUUpRoFUvNaBZHQJ5K0bhm5Dt1fZQoaAZoCWgPQwjjUpW2OO9vQJSGlFKUaBVLzWgWR0CeS85avA45dX2UKGgGaAloD0MICYm0jT8hcUCUhpRSlGgVS+xoFkdAnkv6Rp1zQ3V9lChoBmgJaA9DCIrjwKsljXJAlIaUUpRoFUvpaBZHQJ5MAnYxtYV1fZQoaAZoCWgPQwg9nStKSfBwQJSGlFKUaBVL62gWR0CeTG4MWoFWdX2UKGgGaAloD0MIZyrEI/HlckCUhpRSlGgVS+poFkdAnkzem78Nx3V9lChoBmgJaA9DCMK9Mm9Vu25AlIaUUpRoFUvQaBZHQJ5M/7di2Dx1fZQoaAZoCWgPQwi4kEdwIyNxQJSGlFKUaBVL0mgWR0CeTTGNrCWNdX2UKGgGaAloD0MI44i1+NTPcECUhpRSlGgVS9RoFkdAnk09k4FRpHV9lChoBmgJaA9DCOPD7GUbZ3NAlIaUUpRoFUvLaBZHQJ5NTSb6P811fZQoaAZoCWgPQwhtHLEWn4RzQJSGlFKUaBVNKgFoFkdAnk54Kc/dI3VlLg=="
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 492,
+    "_n_updates": 765,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,
@@ -83,7 +83,7 @@
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "batch_size": 64,
-    "n_epochs": 4,
+    "n_epochs": 5,
     "clip_range": {
         ":type:": "<class 'function'>",
         ":serialized:": "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"

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5392c1c97e71dfd9a6035619d872465fbe6c624422f4473a0efa9f2859bc3c38
-size 87865
+oid sha256:06f4fb737b37a020e510c6d4b987211664beace561486018475ce7d4d3ffa4eb
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:30d69330e05fbf095cb5c382ab1c0ec83d8053d62eae4ecb9af89a3083b865ca
+oid sha256:09b6fd5d9d6c3d4d50f324e90c16cbdcdef3ecbc2c17f2c03c7e483e5d3f0ed7
 size 43201

ppo-LunarLander-v2/system_info.txt

@@ -1,7 +1,7 @@
 OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
-Python: 3.8.15
+Python: 3.8.16
 Stable-Baselines3: 1.6.2
-PyTorch: 1.12.1+cu113
+PyTorch: 1.13.0+cu116
 GPU Enabled: True
 Numpy: 1.21.6
 Gym: 0.21.0

results.json

@@ -1 +1 @@
-{"mean_reward": 280.18417940185105, "std_reward": 18.75865864310711, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-06T14:04:32.083825"}
+{"mean_reward": 255.925552076661, "std_reward": 75.32531167706264, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T19:43:52.462205"}