Tutorial model for Lunar Lander with PPO

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: 283.92 +/- 15.24
+      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 0x780e55bc0f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x780e55bc1000>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x780e55bc1090>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x780e55bc1120>", "_build": "<function ActorCriticPolicy._build at 0x780e55bc11b0>", "forward": "<function ActorCriticPolicy.forward at 0x780e55bc1240>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x780e55bc12d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x780e55bc1360>", "_predict": "<function ActorCriticPolicy._predict at 0x780e55bc13f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x780e55bc1480>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x780e55bc1510>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x780e55bc15a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x780e55bb0940>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 3014656, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1704160688143150460, "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": -0.004885333333333408, "_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": 736, "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": 0.999, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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": "2.1.0+cu121", "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:fa6fe2e77447a8542a7b0c7a6a5dcffe91e8102cf4d93de785b23345a863eb0a
+size 147948

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 0x780e55bc0f70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x780e55bc1000>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x780e55bc1090>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x780e55bc1120>",
+        "_build": "<function ActorCriticPolicy._build at 0x780e55bc11b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x780e55bc1240>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x780e55bc12d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x780e55bc1360>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x780e55bc13f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x780e55bc1480>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x780e55bc1510>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x780e55bc15a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x780e55bb0940>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 3014656,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1704160688143150460,
+    "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": -0.004885333333333408,
+    "_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": 736,
+    "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": 0.999,
+    "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 sha256:8965db676045bb8d6e720398ef9254fd4f1430b70f00cb3103ddb86dc77744bf
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f8c09945bd929fbffd7a14e9fb1e4968a081ccd84fe9e8a4858ce281a926e1b
+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: 2.1.0+cu121
+- 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": 283.9180788, "std_reward": 15.244102187705614, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-02T02:48:13.945338"}