PPO LunarLander-v2 trained agent v1

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: 89.54 +/- 129.18
+      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 0x7f32f66ebeb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f32f66ebf40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f32f64fc040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f32f64fc0d0>", "_build": "<function ActorCriticPolicy._build at 0x7f32f64fc160>", "forward": "<function ActorCriticPolicy.forward at 0x7f32f64fc1f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f32f64fc280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f32f64fc310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f32f64fc3a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f32f64fc430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f32f64fc4c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f32f64fc550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f32f6697040>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1695210465786198194, "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": 280, "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 1024, "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": 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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "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 sha256:6d36d1938cb8f1396b6fbb6e928da6d8d4a85e6a2a13fdde6fb1c4e4558d141a
+size 146752

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 0x7f32f66ebeb0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f32f66ebf40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f32f64fc040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f32f64fc0d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f32f64fc160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f32f64fc1f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f32f64fc280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f32f64fc310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f32f64fc3a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f32f64fc430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f32f64fc4c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f32f64fc550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f32f6697040>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1695210465786198194,
+    "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": 280,
+    "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 1024,
+    "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": 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 sha256:7a75d4fd46d5c76a60187527e967bbf0af5949827b35da17576cc5e56381d5ed
+size 87929

ppo_LunarLander_v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:796037028d92637f4f61cb78845ae3b191987ff53e5cce4159818f7baee004df
+size 43329

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 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 89.53966339999998, "std_reward": 129.17911388824453, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-20T12:16:22.520321"}