Upload PPO LunarLander-v2 trained agent, 2nd version

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 259.43 +/- 13.94
+      value: 281.29 +/- 19.17
       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 0x7f5e4ccdd040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5e4ccdd0d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5e4ccdd160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5e4ccdd1f0>", "_build": "<function ActorCriticPolicy._build at 0x7f5e4ccdd280>", "forward": "<function ActorCriticPolicy.forward at 0x7f5e4ccdd310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5e4ccdd3a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5e4ccdd430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5e4ccdd4c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5e4ccdd550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5e4ccdd5e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5e4ccd74e0>"}, "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": 1673350562698715289, "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": 0.999, "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-5.10.147+-x86_64-with-glibc2.27 #1 SMP Sat Dec 10 16:00:40 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"}}
+{"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 0x7ff9e8b74700>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff9e8b74790>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff9e8b74820>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff9e8b748b0>", "_build": "<function ActorCriticPolicy._build at 0x7ff9e8b74940>", "forward": "<function ActorCriticPolicy.forward at 0x7ff9e8b749d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff9e8b74a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff9e8b74af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff9e8b74b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff9e8b74c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff9e8b74ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff9e8b6dc60>"}, "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:": "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", "n": 4, "_shape": [], "dtype": "int64", "_np_random": "RandomState(MT19937)"}, "n_envs": 32, "num_timesteps": 5046272, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": 1997, "action_noise": null, "start_time": 1673363178455873176, "learning_rate": 0.0002, "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:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.009254400000000107, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 770, "n_steps": 2048, "gamma": 0.995, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 256, "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-5.10.147+-x86_64-with-glibc2.27 #1 SMP Sat Dec 10 16:00:40 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-hpo.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d61411978cb7517cfa4f666739e1a575d19e918709aa97af7e9c29aff07ae659
+size 151450

ppo-LunarLander-v2-hpo/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v2-hpo/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 0x7ff9e8b74700>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff9e8b74790>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff9e8b74820>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff9e8b748b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff9e8b74940>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff9e8b749d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff9e8b74a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff9e8b74af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff9e8b74b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff9e8b74c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff9e8b74ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7ff9e8b6dc60>"
+    },
+    "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:": "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",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "RandomState(MT19937)"
+    },
+    "n_envs": 32,
+    "num_timesteps": 5046272,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": 1997,
+    "action_noise": null,
+    "start_time": 1673363178455873176,
+    "learning_rate": 0.0002,
+    "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:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.009254400000000107,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 770,
+    "n_steps": 2048,
+    "gamma": 0.995,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 256,
+    "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-hpo/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:955e3fb501de280a181a9a31ac47bc845cac408f32dacde6ad1661fe2fc824c7
+size 87929

ppo-LunarLander-v2-hpo/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:073d2a450be79613d785dba604c138b5a5cfb161ad2d838464cacdb1bd8bd30f
+size 43201

ppo-LunarLander-v2-hpo/pytorch_variables.pth

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

ppo-LunarLander-v2-hpo/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.147+-x86_64-with-glibc2.27 #1 SMP Sat Dec 10 16:00:40 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

results.json

@@ -1 +1 @@
-{"mean_reward": 259.42808732006836, "std_reward": 13.944674281487528, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-10T12:07:07.984354"}
+{"mean_reward": 281.2940407705057, "std_reward": 19.165990163768832, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-10T16:23:49.154551"}