Upload PPO LunarLander-v2 trained agent

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 193.96 +/- 43.39
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+  # **PPO** Agent playing **LunarLander-v2**
+  This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+  
+  ## Usage (with Stable-baselines3)
+  TODO: Add your code
+  

config.json

@@ -0,0 +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 0x7f6b77a0a950>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6b77a0a9e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6b77a0aa70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6b77a0ab00>", "_build": "<function ActorCriticPolicy._build at 0x7f6b77a0ab90>", "forward": "<function ActorCriticPolicy.forward at 0x7f6b77a0ac20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6b77a0acb0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6b77a0ad40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6b77a0add0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6b77a0ae60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6b77a0aef0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6b77a567e0>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652993844.2240028, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAABAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": 124, "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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6060bba5798c059d12cd3a6a4d79676fd3a09cfd8504b139cf697075041d99e9
+size 144032

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7f6b77a0a950>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6b77a0a9e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6b77a0aa70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6b77a0ab00>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f6b77a0ab90>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f6b77a0ac20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6b77a0acb0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f6b77a0ad40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6b77a0add0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6b77a0ae60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6b77a0aef0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f6b77a567e0>"
+    },
+    "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": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652993844.2240028,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAABAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": 124,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f4f8e6d4e7c6415c867c0ab3c25a91be03a47d9a045f115563b898478b9bb43f
+size 84829

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a956faa86eb0a5ae82cc7dd29fca742dc60c8c20ea1a97fe9d590c2bdf10ec65
+size 43201

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d746f19e0f42a67dede13a62973586dd737216b74c99c12eb36f6f310695fcb5
+size 170108

results.json

@@ -0,0 +1 @@
+{"mean_reward": 193.9585797946036, "std_reward": 43.39280050361726, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-19T21:12:38.754332"}