upload lunar lander 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: -164.50 +/- 14.26
+      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 0x7efc9aaa30d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efc9aaa3160>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efc9aaa31f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efc9aaa3280>", "_build": "<function ActorCriticPolicy._build at 0x7efc9aaa3310>", "forward": "<function ActorCriticPolicy.forward at 0x7efc9aaa33a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efc9aaa3430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efc9aaa34c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7efc9aaa3550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efc9aaa35e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efc9aaa3670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efc9aaa3700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7efc9aa9b7e0>"}, "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": 229376, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677979655764751135, "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": -0.1468799999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 70, "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": "1.22.4", "Gym": "0.21.0"}}

lunar_lander_model.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ae4b9c104249e78f91e3159d40ff11659fe9cbc0931161361054908c5af0475
+size 147334

lunar_lander_model/_stable_baselines3_version

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

lunar_lander_model/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 0x7efc9aaa30d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efc9aaa3160>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efc9aaa31f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efc9aaa3280>",
+        "_build": "<function ActorCriticPolicy._build at 0x7efc9aaa3310>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7efc9aaa33a0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efc9aaa3430>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efc9aaa34c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7efc9aaa3550>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efc9aaa35e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efc9aaa3670>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efc9aaa3700>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7efc9aa9b7e0>"
+    },
+    "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": 229376,
+    "_total_timesteps": 200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677979655764751135,
+    "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": -0.1468799999999999,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 70,
+    "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
+}

lunar_lander_model/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:959522cc4c0b22ba3d0eeb7b95ec8e01f3f9c812f47aae7f41bec60c5c6f813e
+size 87929

lunar_lander_model/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:478c9e04a0d603578d9af516e7d8d23799eef1ab5f69934eed137ed325dcfdca
+size 43393

lunar_lander_model/pytorch_variables.pth

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

lunar_lander_model/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": -164.50279300125112, "std_reward": 14.259785116022877, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-05T01:35:11.353124"}