Upload PPO LunarLander-v2 trained agent

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 288.16 +/- 11.37
+      name: mean_reward
+      verified: false
+---
+
+# **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
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

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 0x7f6103cba9d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6103cbaa60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6103cbaaf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6103cbab80>", "_build": "<function ActorCriticPolicy._build at 0x7f6103cbac10>", "forward": "<function ActorCriticPolicy.forward at 0x7f6103cbaca0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6103cbad30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6103cbadc0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6103cbae50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6103cbaee0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6103cbaf70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6103cb58d0>"}, "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": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671125222015244064, "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": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 368, "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": "3.8.16", "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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4bca22d7f30ba3804203d432d91c826ffb98977ec5c1bfe0e253ff78dbdba3f4
+size 147170

ppo-LunarLander-v2/_stable_baselines3_version

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

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 0x7f6103cba9d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6103cbaa60>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6103cbaaf0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6103cbab80>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f6103cbac10>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f6103cbaca0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6103cbad30>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f6103cbadc0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6103cbae50>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6103cbaee0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6103cbaf70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f6103cb58d0>"
+    },
+    "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": 1507328,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1671125222015244064,
+    "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": -0.004885333333333408,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 368,
+    "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 sha256:528cecc726732b7061513cd043b4c961b79f5efdec0a2f24459f795280e3adfe
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93b1938c502a9fb8ffa07871347036bd4831f3f8f1f6ef237c9b7418902a6368
+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-5.10.133+-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

results.json

@@ -0,0 +1 @@
+{"mean_reward": 288.1556835202368, "std_reward": 11.370564258523801, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-15T17:53:56.339743"}