First lunar lander

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: 255.74 +/- 14.52
+      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 0x7fd97ad47e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd97ad47ee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd97ad47f70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd97ad4c040>", "_build": "<function ActorCriticPolicy._build at 0x7fd97ad4c0d0>", "forward": "<function ActorCriticPolicy.forward at 0x7fd97ad4c160>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd97ad4c1f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd97ad4c280>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd97ad4c310>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd97ad4c3a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd97ad4c430>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd97ad4c4c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd97ad491b0>"}, "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:": "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", "n": 4, "_shape": [], "dtype": "int64", "_np_random": "RandomState(MT19937)"}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676325041318851995, "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": 310, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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"}}

first_lunar_lander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ac8063b3179d7022aad4dd29e422be0868732fb3b42530c4f18c7fcbaca6f1d
+size 151005

first_lunar_lander/_stable_baselines3_version

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

first_lunar_lander/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 0x7fd97ad47e50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd97ad47ee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd97ad47f70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd97ad4c040>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fd97ad4c0d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fd97ad4c160>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd97ad4c1f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd97ad4c280>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fd97ad4c310>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd97ad4c3a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd97ad4c430>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd97ad4c4c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fd97ad491b0>"
+    },
+    "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:": "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",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "RandomState(MT19937)"
+    },
+    "n_envs": 16,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676325041318851995,
+    "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": 310,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

first_lunar_lander/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d750a88198e9dda00af812e6111c5424043cecf15b9cd7975459a06740689a5
+size 87929

first_lunar_lander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:086b5497cfb27facad17f1e7728b4835e85f7d681ae63740ec44738764a44c64
+size 43393

first_lunar_lander/pytorch_variables.pth

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

first_lunar_lander/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: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 255.7379125179603, "std_reward": 14.516738193532168, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-13T22:28:28.966287"}