Upload Trained PPO model for Lunar Lander v2

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: 274.20 +/- 22.46
+      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 0x7eca718f4a60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eca718f4af0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eca718f4b80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7eca718f4c10>", "_build": "<function ActorCriticPolicy._build at 0x7eca718f4ca0>", "forward": "<function ActorCriticPolicy.forward at 0x7eca718f4d30>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7eca718f4dc0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7eca718f4e50>", "_predict": "<function ActorCriticPolicy._predict at 0x7eca718f4ee0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7eca718f4f70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7eca718f5000>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7eca718f5090>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7eca7188b640>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1713779389823867923, "learning_rate": 0.0003, "tensorboard_log": null, "_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": 331, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c24932fb3ff78df84d447403c95f6aba6e8c2004db236b74c1d2bb090e6ad8e9
+size 148048

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 0x7eca718f4a60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eca718f4af0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eca718f4b80>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7eca718f4c10>",
+        "_build": "<function ActorCriticPolicy._build at 0x7eca718f4ca0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7eca718f4d30>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7eca718f4dc0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7eca718f4e50>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7eca718f4ee0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7eca718f4f70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7eca718f5000>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7eca718f5090>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7eca7188b640>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1713779389823867923,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_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": 331,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:096093a631cf44b7b66595b5ac63342a2ab2c39aae101eba6979dbc1dc63c47b
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f96e9073b4b3c76f125d481ceff0b2f527561c3405bf3cc75a514126244fe774
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 274.19683640000005, "std_reward": 22.45705347895883, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-22T10:37:12.407195"}