Trained with 1000000 steps

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 174.89 +/- 72.18
+      value: 254.01 +/- 50.57
       name: mean_reward
     task:
       type: reinforcement-learning

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 0x7f545f1121f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f545f112280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f545f112310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f545f1123a0>", "_build": "<function ActorCriticPolicy._build at 0x7f545f112430>", "forward": "<function ActorCriticPolicy.forward at 0x7f545f1124c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f545f112550>", "_predict": "<function ActorCriticPolicy._predict at 0x7f545f1125e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f545f112670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f545f112700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f545f112790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f545f111b00>"}, "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": 524288, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651745918.9802494, "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": -0.04857599999999995, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 160, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWV9wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjGMvaG9tZS9kaXNrc2hpbWEvLnB5ZW52L3ZlcnNpb25zLzMuOS42L2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjGMvaG9tZS9kaXNrc2hpbWEvLnB5ZW52L3ZlcnNpb25zLzMuOS42L2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoIH2UfZQoaBdoDowMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBiMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.0-109-generic-x86_64-with-glibc2.27 #123~18.04.1-Ubuntu SMP Fri Apr 8 09:48:52 UTC 2022", "Python": "3.9.6", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu102", "GPU Enabled": "True", "Numpy": "1.21.3", "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 0x7fa70d43be50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa70d43bee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa70d43bf70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa70d3bd040>", "_build": "<function ActorCriticPolicy._build at 0x7fa70d3bd0d0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa70d3bd160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa70d3bd1f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa70d3bd280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa70d3bd310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa70d3bd3a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa70d3bd430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa70d3be180>"}, "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": 1651839706.973791, "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": 310, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.0-109-generic-x86_64-with-glibc2.27 #123~18.04.1-Ubuntu SMP Fri Apr 8 09:48:52 UTC 2022", "Python": "3.9.6", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu102", "GPU Enabled": "True", "Numpy": "1.21.3", "Gym": "0.21.0"}}

ppo-LunarLander-v2-1000000-steps.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e36f0a55f2a998d2422d21947965fd0acd29b18a8ccb9832bee6082b40470dc7
+size 144191

ppo-LunarLander-v2-1000000-steps/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

ppo-LunarLander-v2-1000000-steps/data

@@ -0,0 +1,94 @@
+{
+    "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 0x7fa70d43be50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa70d43bee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa70d43bf70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa70d3bd040>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa70d3bd0d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa70d3bd160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa70d3bd1f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa70d3bd280>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa70d3bd310>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa70d3bd3a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa70d3bd430>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fa70d3be180>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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": 1651839706.973791,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWV9wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjGMvaG9tZS9kaXNrc2hpbWEvLnB5ZW52L3ZlcnNpb25zLzMuOS42L2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjGMvaG9tZS9kaXNrc2hpbWEvLnB5ZW52L3ZlcnNpb25zLzMuOS42L2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoIH2UfZQoaBdoDowMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBiMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPzOpKjBVMmGFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    },
+    "_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:": "gAWVOhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI36eq0MAgcUCUhpRSlIwBbJRNSAGMAXSUR0ChVLpK8L8adX2UKGgGaAloD0MIa7ddaO6KcECUhpRSlGgVS9BoFkdAoVU8NH6MznV9lChoBmgJaA9DCMu/lldu3XBAlIaUUpRoFUvMaBZHQKFVS1ZTyax1fZQoaAZoCWgPQwiTVRFuMplxQJSGlFKUaBVNMwFoFkdAoVYKq6vq1XV9lChoBmgJaA9DCL05XKt9X3NAlIaUUpRoFUvYaBZHQKFWIsbNr0t1fZQoaAZoCWgPQwiARunSP59wQJSGlFKUaBVNrgFoFkdAoVYr7ALy+nV9lChoBmgJaA9DCLcpHhcVA3NAlIaUUpRoFU0QAWgWR0ChVoRI8QqadX2UKGgGaAloD0MIkGrY70mAcECUhpRSlGgVS+RoFkdAoVaa5AhStXV9lChoBmgJaA9DCERPyqSGejdAlIaUUpRoFUu1aBZHQKFWxEG7jDN1fZQoaAZoCWgPQwhHV+nuuqtyQJSGlFKUaBVL3WgWR0ChVzqa5PM0dX2UKGgGaAloD0MIPuqvVxggc0CUhpRSlGgVTWABaBZHQKFXhg5zYEp1fZQoaAZoCWgPQwhbQj7o2eRuQJSGlFKUaBVL7GgWR0ChV5rCm/FjdX2UKGgGaAloD0MI78UX7bFdcUCUhpRSlGgVTRYBaBZHQKFYK7HQyAR1fZQoaAZoCWgPQwjIJvkRP/dwQJSGlFKUaBVL2GgWR0ChWIwLNOdodX2UKGgGaAloD0MItAOuK+YackCUhpRSlGgVTQQBaBZHQKFZMlJHy3F1fZQoaAZoCWgPQwiPUZ55OfNhQJSGlFKUaBVN6ANoFkdAoVl0XHim23V9lChoBmgJaA9DCDiHa7XHiHFAlIaUUpRoFUvFaBZHQKFZimJFb3Z1fZQoaAZoCWgPQwiOIQA49qdtQJSGlFKUaBVL3mgWR0ChWZHiNsFddX2UKGgGaAloD0MI5dGNsGjpcECUhpRSlGgVS+poFkdAoVmz7ZWaMXV9lChoBmgJaA9DCAK5xJHHoHBAlIaUUpRoFUv5aBZHQKFZ0tLcsUZ1fZQoaAZoCWgPQwjj/bj98iZiQJSGlFKUaBVN6ANoFkdAoVoGzY287XV9lChoBmgJaA9DCL+AXrizinFAlIaUUpRoFUvCaBZHQKFaIbrC3w11fZQoaAZoCWgPQwj3WWWmdLVxQJSGlFKUaBVL7WgWR0ChWiOdGy5adX2UKGgGaAloD0MI/N8RFWoDdECUhpRSlGgVS/hoFkdAoVpiwMYuTXV9lChoBmgJaA9DCHqmlxhLKWxAlIaUUpRoFUvQaBZHQKFalPwd8zB1fZQoaAZoCWgPQwguO8Q/7IxwQJSGlFKUaBVLwWgWR0ChWtwztTkydX2UKGgGaAloD0MIeQd40sLZbUCUhpRSlGgVS+5oFkdAoVriwB5ooXV9lChoBmgJaA9DCBd+cD61M3BAlIaUUpRoFUu7aBZHQKFbxovBacJ1fZQoaAZoCWgPQwgD7KNTV41wQJSGlFKUaBVL02gWR0ChXF5Cv5gxdX2UKGgGaAloD0MIS80eaEWRcECUhpRSlGgVS+BoFkdAoVxqKk2xZHV9lChoBmgJaA9DCGWmtP5WSHFAlIaUUpRoFUv6aBZHQKFcc1He7+V1fZQoaAZoCWgPQwiduByvQFFvQJSGlFKUaBVLxWgWR0ChXKWKEWZadX2UKGgGaAloD0MIPbfQlYitbkCUhpRSlGgVS99oFkdAoVzhyjpLVXV9lChoBmgJaA9DCHDurx738UlAlIaUUpRoFUvEaBZHQKFc8FA3T/h1fZQoaAZoCWgPQwjRBmADIkNuQJSGlFKUaBVL6WgWR0ChXSH7gsK9dX2UKGgGaAloD0MIfJ3Ul6VdQ0CUhpRSlGgVS9ZoFkdAoV1m1rqMWHV9lChoBmgJaA9DCOM1r+qsInJAlIaUUpRoFU1QAWgWR0ChXeyrHU+cdX2UKGgGaAloD0MIfgIoRhYMcECUhpRSlGgVTTMBaBZHQKFfIlRgqmV1fZQoaAZoCWgPQwgU0a+tX9dwQJSGlFKUaBVL7mgWR0ChXzZhjOLSdX2UKGgGaAloD0MIZ2Ml5lmmbUCUhpRSlGgVS+NoFkdAoV+vtrsSkHV9lChoBmgJaA9DCHnMQGV8yHBAlIaUUpRoFUvjaBZHQKFfxQO4G2V1fZQoaAZoCWgPQwgbZJKRs5VyQJSGlFKUaBVL/WgWR0ChYCLl/6O6dX2UKGgGaAloD0MInfaUnNOacECUhpRSlGgVS+VoFkdAoWCVtl7MPnV9lChoBmgJaA9DCEuUvaWccXJAlIaUUpRoFUv0aBZHQKFgmptrKvF1fZQoaAZoCWgPQwhFK/cCMwlxQJSGlFKUaBVL/GgWR0ChYKrRSgoPdX2UKGgGaAloD0MIZOjYQSVScUCUhpRSlGgVS9BoFkdAoWEsi8nNPnV9lChoBmgJaA9DCO2ePCzUH3FAlIaUUpRoFU0JAWgWR0ChYXryMDOkdX2UKGgGaAloD0MI527XS1MscUCUhpRSlGgVS+NoFkdAoWLJxrBTGnV9lChoBmgJaA9DCBLZB1kWVmdAlIaUUpRoFU3oA2gWR0ChYvJlJ6IFdX2UKGgGaAloD0MI7KUpAhxqbkCUhpRSlGgVS91oFkdAoWNM1uR9w3V9lChoBmgJaA9DCNI6qpqgo29AlIaUUpRoFUvGaBZHQKFjaFj/dZd1fZQoaAZoCWgPQwi2gxH7BBRwQJSGlFKUaBVNCAFoFkdAoWOCK508vHV9lChoBmgJaA9DCClcj8J1pmFAlIaUUpRoFU3oA2gWR0ChY4Zpi7TVdX2UKGgGaAloD0MIineAJ+2acECUhpRSlGgVS+5oFkdAoWOpO8Cgb3V9lChoBmgJaA9DCFCop49ABHFAlIaUUpRoFUvAaBZHQKFjz5AyEct1fZQoaAZoCWgPQwgwEW+dfwFvQJSGlFKUaBVL8GgWR0ChZHWvr4WUdX2UKGgGaAloD0MI3CqIge7vcECUhpRSlGgVS8ZoFkdAoWSo6nzg/HV9lChoBmgJaA9DCOJ0kq1u4XJAlIaUUpRoFUvPaBZHQKFmC2ycCo11fZQoaAZoCWgPQwhI/fUKy2xxQJSGlFKUaBVNWgFoFkdAoWYf6fra/XV9lChoBmgJaA9DCFwC8E8psHFAlIaUUpRoFUvCaBZHQKFmalhPTG51fZQoaAZoCWgPQwifAIqRpddvQJSGlFKUaBVLx2gWR0ChZr/qxC6ZdX2UKGgGaAloD0MIF5rrNNIDbkCUhpRSlGgVS99oFkdAoWb5y6tknXV9lChoBmgJaA9DCAvPS8XGF3FAlIaUUpRoFUvwaBZHQKFnDQSBbwB1fZQoaAZoCWgPQwgGLo814xNwQJSGlFKUaBVL52gWR0ChZx3P7el9dX2UKGgGaAloD0MIo8nFGJhBc0CUhpRSlGgVTQkBaBZHQKFnHiTdLxt1fZQoaAZoCWgPQwjH2AkvQV1vQJSGlFKUaBVNKgNoFkdAoWctZcLSeHV9lChoBmgJaA9DCM7g7xczZ2NAlIaUUpRoFU3oA2gWR0ChZ3RT850bdX2UKGgGaAloD0MIUS/4NKfGcECUhpRSlGgVS9FoFkdAoWfoJkXk53V9lChoBmgJaA9DCLrZHyh3YXNAlIaUUpRoFU3PAWgWR0ChaHCZnctYdX2UKGgGaAloD0MIS3LAribyY0CUhpRSlGgVTegDaBZHQKFopg5R0lt1fZQoaAZoCWgPQwhD5PT1fFpxQJSGlFKUaBVNLgFoFkdAoWkMbgjyF3V9lChoBmgJaA9DCC/9S1LZIHBAlIaUUpRoFUvcaBZHQKFpYjlgc951fZQoaAZoCWgPQwjIz0au259yQJSGlFKUaBVLy2gWR0ChaWVjRUm2dX2UKGgGaAloD0MIXOffLrvvcECUhpRSlGgVS89oFkdAoWoMKkVN6HV9lChoBmgJaA9DCOLK2TujyG5AlIaUUpRoFUvPaBZHQKFqHOqvNeN1fZQoaAZoCWgPQwiMZI9QM99yQJSGlFKUaBVNHAFoFkdAoWpD9Oymh3V9lChoBmgJaA9DCD25pkAmy3FAlIaUUpRoFUvXaBZHQKFqSowVTJh1fZQoaAZoCWgPQwiRZFbvMJZyQJSGlFKUaBVL2GgWR0ChapIwudwvdX2UKGgGaAloD0MIWyIXnAFCcUCUhpRSlGgVTUUBaBZHQKFrtFiKBNF1fZQoaAZoCWgPQwj6DKg3o61vQJSGlFKUaBVNDwFoFkdAoWvfO4XoDHV9lChoBmgJaA9DCEXURJ+Pwl5AlIaUUpRoFU3oA2gWR0Cha+XyqdYodX2UKGgGaAloD0MImE2AYXmjcUCUhpRSlGgVS+5oFkdAoWvt4JNTLnV9lChoBmgJaA9DCLhAguKHQnBAlIaUUpRoFUvxaBZHQKFsKTX8O091fZQoaAZoCWgPQwixahDmdjlwQJSGlFKUaBVL42gWR0ChbLeIMz/IdX2UKGgGaAloD0MIaObJNcV8cUCUhpRSlGgVTREBaBZHQKFtDf8/D+B1fZQoaAZoCWgPQwjI68GkuE1wQJSGlFKUaBVL/WgWR0ChbRp8neBQdX2UKGgGaAloD0MInu488Ry9b0CUhpRSlGgVS95oFkdAoW1MExIrfHV9lChoBmgJaA9DCExsPq4N/W9AlIaUUpRoFUvRaBZHQKFtosDnvDx1fZQoaAZoCWgPQwh4J58eWxdwQJSGlFKUaBVL82gWR0Chbdh42S+ydX2UKGgGaAloD0MICyjU0weeckCUhpRSlGgVTRYBaBZHQKFuU0rsjVx1fZQoaAZoCWgPQwhgrkULkPxxQJSGlFKUaBVL7GgWR0ChbzIHLRrrdX2UKGgGaAloD0MIQDOID2x6c0CUhpRSlGgVS+NoFkdAoW83hVENOXV9lChoBmgJaA9DCKd1G9Q+enFAlIaUUpRoFUvxaBZHQKFvfPC2tuF1fZQoaAZoCWgPQwhN2H4yRiRwQJSGlFKUaBVL/mgWR0Chb6i3PRiPdX2UKGgGaAloD0MIKsql8QsCcUCUhpRSlGgVS+1oFkdAoW+xT850bXV9lChoBmgJaA9DCKRUwhO6qHFAlIaUUpRoFUvRaBZHQKFwNjslb/x1fZQoaAZoCWgPQwi+3v3xXrdvQJSGlFKUaBVL5mgWR0ChcLpZW7vodX2UKGgGaAloD0MIrBqEuR0PcUCUhpRSlGgVS+5oFkdAoXFtDSgGr3V9lChoBmgJaA9DCFLVBFE33nBAlIaUUpRoFUvYaBZHQKFxmxoqTbF1fZQoaAZoCWgPQwhCWmPQySpxQJSGlFKUaBVNJwFoFkdAoXIf99+gDnVlLg=="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 310,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2-1000000-steps/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:187018ce077611f782fe29549b324867aa31f02f5503828fde560102c622df40
+size 84893

ppo-LunarLander-v2-1000000-steps/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83b960b28b1568bc88c43c6b2c0aee1c72efee330f8196f7fc9baf83f4276738
+size 43201

ppo-LunarLander-v2-1000000-steps/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-LunarLander-v2-1000000-steps/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.0-109-generic-x86_64-with-glibc2.27 #123~18.04.1-Ubuntu SMP Fri Apr 8 09:48:52 UTC 2022
+Python: 3.9.6
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu102
+GPU Enabled: True
+Numpy: 1.21.3
+Gym: 0.21.0

replay.mp4

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0b2fc90bddda121b7bed25324dbf5bf81368be70837c888dcc6737d4147bfe37
-size 229969
+oid sha256:1c2dd903dc0d90b4c72cf4d4bf3f61d165f2ee39d88c5223d08f169489df659e
+size 208685

results.json

@@ -1 +1 @@
-{"mean_reward": 174.88995257066836, "std_reward": 72.17645805553178, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-05T23:54:14.178519"}
+{"mean_reward": 254.00747777412735, "std_reward": 50.57035457045583, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T22:55:14.869037"}