first Lunar Landar 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: 247.07 +/- 39.96
+      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 0x7f23c1da3c70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f23c1da3d00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f23c1da3d90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f23c1da3e20>", "_build": "<function ActorCriticPolicy._build at 0x7f23c1da3eb0>", "forward": "<function ActorCriticPolicy.forward at 0x7f23c1da3f40>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f23c1da8040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f23c1da80d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f23c1da8160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f23c1da81f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f23c1da8280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f23c1da8310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f2361035640>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682953667345407341, "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, "_stats_window_size": 100, "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, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

doxRl.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2e9b653a2abe1e3b578d9307ff9ac58c6a7acf6158d39d948d5f8cfbe3e7f71
+size 147443

doxRl/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

doxRl/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7f23c1da3c70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f23c1da3d00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f23c1da3d90>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f23c1da3e20>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f23c1da3eb0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f23c1da3f40>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f23c1da8040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f23c1da80d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f23c1da8160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f23c1da81f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f23c1da8280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f23c1da8310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f2361035640>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1682953667345407341,
+    "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,
+    "_stats_window_size": 100,
+    "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,
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVpQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoC0sIhZSMAUOUdJRSlIwEaGlnaJRoEyiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoC0sIhZRoFnSUUpSMDWJvdW5kZWRfYmVsb3eUaBMolggAAAAAAAAAAAAAAAAAAACUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYIAAAAAAAAAAAAAAAAAAAAlGgiSwiFlGgWdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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
+}

doxRl/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf2ea2d30792a492b5f7d17207814ed7b9149be770fee3f1df62eb9e7cb25574
+size 88057

doxRl/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a62ea1a4b116910a9faa63e2ade00136cf7760ae863517bb715db345fb60b9a1
+size 43329

doxRl/pytorch_variables.pth

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

doxRl/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.10.11
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 247.0735274654799, "std_reward": 39.957427407891466, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-01T16:26:33.517930"}