PPO agent for LunarLander-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: 251.70 +/- 23.28
+      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 0x7898192be7a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7898192be830>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7898192be8c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7898192be950>", "_build": "<function ActorCriticPolicy._build at 0x7898192be9e0>", "forward": "<function ActorCriticPolicy.forward at 0x7898192bea70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7898192beb00>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7898192beb90>", "_predict": "<function ActorCriticPolicy._predict at 0x7898192bec20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7898192becb0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7898192bed40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7898192bedd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x789838acd7c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1727059502954982956, "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": 248, "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.4.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "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 sha256:0aec3f820d64a1c70de3e95777ab968549871a39e610058e95e511645df9352b
+size 148076

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 0x7898192be7a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7898192be830>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7898192be8c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7898192be950>",
+        "_build": "<function ActorCriticPolicy._build at 0x7898192be9e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7898192bea70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7898192beb00>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7898192beb90>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7898192bec20>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7898192becb0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7898192bed40>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7898192bedd0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x789838acd7c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1727059502954982956,
+    "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": 248,
+    "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 sha256:595e9072d1723544b67910deb7321ffe09216383589140af80a49575d1e9cf72
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77d6b3a1bfe4992828f6a4e0497555be9e2e133d533c9299e32edf57473a38a9
+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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.4.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 251.69833709999997, "std_reward": 23.275695337834158, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-09-23T03:08:22.519328"}