Push LunarLander-v2 model

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: 245.82 +/- 20.59
+      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 0x7fc1cab071f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc1cab07280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc1cab07310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc1cab073a0>", "_build": "<function ActorCriticPolicy._build at 0x7fc1cab07430>", "forward": "<function ActorCriticPolicy.forward at 0x7fc1cab074c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc1cab07550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc1cab075e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc1cab07670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc1cab07700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc1cab07790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc1cab07820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc1cab0a030>"}, "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": 1678015469658385569, "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-glibc2.29 # 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": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc3b4787de05e6e950c5f5e6f1b60efabf6bbfe756c15330d44499e989097c9f
+size 147424

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7fc1cab071f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc1cab07280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc1cab07310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc1cab073a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fc1cab07430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fc1cab074c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc1cab07550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc1cab075e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fc1cab07670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc1cab07700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc1cab07790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc1cab07820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fc1cab0a030>"
+    },
+    "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": 1678015469658385569,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAGa4ZrxSYLa5Hgatux3eiDbEhJI66iL9tQAAgD8AAIA/ZtelPOGwtbq8GwC5q3Ljs+gS27nSAxI4AACAPwAAgD/m4Zk+eeiKP97nqT5sfZ6+WRt6Pt0acr0AAAAAAAAAAM1aIL2PLni6doU2tiVoxrEM5kA7ixBeNQAAgD8AAIA/zfTrO4+aS7rXrS84whR3MwIqC7vb5Ey3AACAPwAAgD9NlUG9w8UwumnnuzpQaQw2sWMIO6p42LkAAIA/AACAP80jmzwUfIi6XTS1uyq4kTUwMTm7kP/9tAAAgD8AAIA/ZgRhvY+aM7qG1YK4T5EFMyf2B7sQgZc3AACAPwAAgD8qu4s+xl1ZP03Etr1wGpG+ebDVPQ6xC74AAAAAAAAAABrHmb1co0q6jm4eOprGCDVmZk+6FOU6uQAAgD8AAIA/M0RavY9WfbrOao07axtHNiZ8uTlgAaS6AACAPwAAgD8zUzw9H+WOOK4anjr1xbc1fx62O+GivbkAAIA/AACAP4Boer2Fm9+52vA8uNosg7J3Tmq7wl9hNwAAgD8AAIA/kx1gPjCXNj98YJa9lTIovmh3bT3DcC49AAAAAAAAAACaYtu84eKJuvqPhzor1SQ1WjQNu9KLnbkAAIA/AACAP2aO+7tcC1K6g+2UOq9+ajVNG2m6anaouQAAgD8AAIA/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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d869dda0a418d71a6b8353d4a3f8a8502c4e09939ae1498877481d0f015d10c8
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:300b806dd66761c4b9f9b6f04d8b5649e1cc0999564036e66bb069530164b52c
+size 43393

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-5.10.147+-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: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 245.82138147918045, "std_reward": 20.592956137641043, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-05T12:02:01.607550"}