Upload v5 PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 245.01 +/- 20.78
+      value: 286.56 +/- 21.61
       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 0x7f6da2dcedc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6da2dcee50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6da2dceee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6da2dcef70>", "_build": "<function ActorCriticPolicy._build at 0x7f6da2d52040>", "forward": "<function ActorCriticPolicy.forward at 0x7f6da2d520d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6da2d52160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6da2d521f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6da2d52280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6da2d52310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6da2d523a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6da2d52430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6da2dcd390>"}, "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": 1676588005706682054, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAACWsx76aMyI/my+YvcTob750eG2+OuipPQAAAAAAAAAAzVppvK4DMj9OjqA6QlyxvuU977xNLKU8AAAAAAAAAACaekO+4zsqPzWYe72VsJO+AukSvoUzHL0AAAAAAAAAADMgM73WzKM/zqm+vd/M0r7w4oC9QhzGvQAAAAAAAAAAUwm7vkNsfz+wsmy7/m/BvoPxm77sVhY+AAAAAAAAAAAz/Si8LL3MPLXxpTzaw2i+U2gSPV39hT0AAAAAAAAAAHMIl7772TI/DTaovKY0oL6DjF2+1TP0PQAAAAAAAAAAGgV/PeovOT/IPki9KxSQvhwpxjylRLC9AAAAAAAAAAANTzM+kPaePrMQMb6F/3a+LHqjPZ0sXbwAAAAAAAAAAHPu0r3kXM0+OFvBPe0Ahb6gLiE8uVUuvQAAAAAAAAAAGl5+vSnYTz0Kz7Y98cEhviF7TT0qZYe9AAAAAAAAAAAzaxI7wfyjPp2i2rxccqm+u0aHvc5AZbwAAAAAAAAAAM2boTz28CS6whWBMir+IKpxqSS7K9dwswAAgD8AAIA/zYy5u8DCtT9M1hK/wr3OPtk71zsWCwU+AAAAAAAAAADNPY49eK3cPnh3UL10jpC+qvYpu6x+qjsAAAAAAAAAAKC0Uz7zlkY/BoMOvsHUpL5H0wY7x65HPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAABAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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-glibc2.29 # 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 0x7fba9ea11dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fba9ea11e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fba9ea11ee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fba9ea11f70>", "_build": "<function ActorCriticPolicy._build at 0x7fba9ea15040>", "forward": "<function ActorCriticPolicy.forward at 0x7fba9ea150d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fba9ea15160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fba9ea151f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fba9ea15280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fba9ea15310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fba9ea153a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fba9ea15430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fba9ea103c0>"}, "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": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676594519363908547, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAABAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": 560, "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-glibc2.29 # 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"}}

ppo_lunar.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3d8b85f06452ea0b757376ea2423b54c934cc9ef48a916bf9553dee8c1201a4a
-size 147420
+oid sha256:dae5a95d4f52adae7a09a5098ae7c418e1b449c63d82de35dd4299105a20ec8a
+size 147430

ppo_lunar/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 0x7f6da2dcedc0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6da2dcee50>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6da2dceee0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6da2dcef70>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f6da2d52040>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f6da2d520d0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6da2d52160>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6da2d521f0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f6da2d52280>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6da2d52310>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6da2d523a0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6da2d52430>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fba9ea11dc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fba9ea11e50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fba9ea11ee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fba9ea11f70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fba9ea15040>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fba9ea150d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fba9ea15160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fba9ea151f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fba9ea15280>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fba9ea15310>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fba9ea153a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fba9ea15430>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f6da2dcd390>"
+        "_abc_impl": "<_abc_data object at 0x7fba9ea103c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -44,11 +44,11 @@
     },
     "n_envs": 16,
     "num_timesteps": 1015808,
-    "_total_timesteps": 1000000,
+    "_total_timesteps": 1000000.0,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1676588005706682054,
+    "start_time": 1676594519363908547,
     "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:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -70,13 +70,13 @@
     "_current_progress_remaining": -0.015808000000000044,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 248,
+    "_n_updates": 560,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,

ppo_lunar/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:036db2c90e96e6a526f0766fbbc38a4e6e1ea6eb437f981f58732bca2731d1ad
-size 87929
+oid sha256:589a55a6562774a0be438794297386ccdc83be2fc7c28309d7f91a5049c4ebff
+size 88057

ppo_lunar/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dcf6ee3b3a1af09c4187bef048e9918308bbc533a751ce8ea21eab4300efd780
+oid sha256:798fbb1c16ceff61bbcf029be4dd4c719dff28697d3e9b1ee8e9672efcbf0d57
 size 43393

results.json

@@ -1 +1 @@
-{"mean_reward": 245.00877779890578, "std_reward": 20.7816379372207, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-16T23:21:58.505502"}
+{"mean_reward": 286.55529812630493, "std_reward": 21.60824627295567, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-17T01:10:30.120453"}