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: 248.73 +/- 18.61
+      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 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 0x00000221367D2AF0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x00000221367D2B80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x00000221367D2C10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x00000221367D2CA0>", "_build": "<function ActorCriticPolicy._build at 0x00000221367D2D30>", "forward": "<function ActorCriticPolicy.forward at 0x00000221367D2DC0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x00000221367D2E50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x00000221367D2EE0>", "_predict": "<function ActorCriticPolicy._predict at 0x00000221367D2F70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x00000221367D4040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x00000221367D40D0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x00000221367D4160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x00000221367D1F00>"}, "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1680530290864710200, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAKYDD76f2r27jqEAu+5GlbgfZgo9SLogOgAAgD8AAIA/YIQ5vq0btD4mIYU+EfZ1vrvCFj0WzzE9AAAAAAAAAABGz0A+h3EOP9DP0711s42+BzmSPYFqGL0AAAAAAAAAAPoNBL42qCk/pUF3PVF1sL6vuQK9ZFWJPAAAAAAAAAAA5qZyvVxDEbpAo9u0lR46r9+JvDoaXCc0AACAPwAAgD+aEL+9zwCSP4tps70BwMS+KRO5vbu4az0AAAAAAAAAAJqpNTukzAc88j0jO2PCMr6K9o+9bjvPPQAAAAAAAAAAZsqmO/fRGj8N1G889dx4vqtjJL1MALe8AAAAAAAAAACaHcw8z0MgvLYKeb3PD7i96P+aPSHrmT4AAIA/AACAP/PdgL32VAa6DvdStRE1VbABjK07UwxzNAAAgD8AAIA/zYDrvK6/k7oKCDE0P0GOr+xFDLvPNKazAACAPwAAgD8Atti8fjbfPtJmw73NVJ2+VcbkO9DEI70AAAAAAAAAAGbGA7vs5TA+NimDPfc0Lr6G3Ko9KhDgPAAAAAAAAAAAgDx7va6HmLoxKBm1g4o0sNlsDLuyvWM0AACAPwAAgD/aeN+9FCu8Pvs+qD1TV3++/aVLvaNCzbwAAAAAAAAAAGagqb2PLl26Y29SuoDCDLbc/sY6VSZ1OQAAAAAAAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": "Windows-10-10.0.22621-SP0 10.0.22621", "Python": "3.9.13", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cpu", "GPU Enabled": "False", "Numpy": "1.23.5", "Gym": "0.21.0"}}

ppo-LunarLander-v2-1000000.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d32f0f863272ba1d37d8a22531ad38cffa13856547d13425ed518a378b83eb5
+size 146862

ppo-LunarLander-v2-1000000/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2-1000000/data

@@ -0,0 +1,95 @@
+{
+    "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 0x00000221367D2AF0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x00000221367D2B80>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x00000221367D2C10>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x00000221367D2CA0>",
+        "_build": "<function ActorCriticPolicy._build at 0x00000221367D2D30>",
+        "forward": "<function ActorCriticPolicy.forward at 0x00000221367D2DC0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x00000221367D2E50>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x00000221367D2EE0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x00000221367D2F70>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x00000221367D4040>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x00000221367D40D0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x00000221367D4160>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x00000221367D1F00>"
+    },
+    "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////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": 1680530290864710200,
+    "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
+}

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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:531257054525b81e844d308c33269a1d597f2ac5b7520c226a5c2517c94d2590
+size 87545

ppo-LunarLander-v2-1000000/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6e5295ccc5e1bea0abb58a692176ab04ea89161cecec0fdbdfa45f9630fae8c
+size 43265

ppo-LunarLander-v2-1000000/pytorch_variables.pth

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

ppo-LunarLander-v2-1000000/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Windows-10-10.0.22621-SP0 10.0.22621
+- Python: 3.9.13
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cpu
+- GPU Enabled: False
+- Numpy: 1.23.5
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 248.73077629916014, "std_reward": 18.606913667523582, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-03T18:03:28.471566"}