PPO lunarLander agent

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: 253.59 +/- 17.24
+      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 0x78f29515d750>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78f29515d7e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78f29515d870>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78f29515d900>", "_build": "<function ActorCriticPolicy._build at 0x78f29515d990>", "forward": "<function ActorCriticPolicy.forward at 0x78f29515da20>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78f29515dab0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78f29515db40>", "_predict": "<function ActorCriticPolicy._predict at 0x78f29515dbd0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78f29515dc60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78f29515dcf0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78f29515dd80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78f2950ee000>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1710319353536497389, "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:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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": "2.1.0+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 sha256:128bf8a43b43580baa428e67e653375f0ca6cac65f0ad68059f78d39c35e83b7
+size 148080

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 0x78f29515d750>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78f29515d7e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78f29515d870>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78f29515d900>",
+        "_build": "<function ActorCriticPolicy._build at 0x78f29515d990>",
+        "forward": "<function ActorCriticPolicy.forward at 0x78f29515da20>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x78f29515dab0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78f29515db40>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x78f29515dbd0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78f29515dc60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78f29515dcf0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x78f29515dd80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78f2950ee000>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1710319353536497389,
+    "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 sha256:757da7a5fd42c4dc1ac0c73022ff392dbf33879fb221399b166f4c68cf0d2d14
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa9345c0d492c335445787f89780a8919595e350e391dcf791d7022bb85b3155
+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: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 253.59168910000002, "std_reward": 17.23734988786091, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-13T09:12:14.175610"}