LunarLander 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: 279.70 +/- 19.66
+      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 0x7f4941263b50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4941263be0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4941263c70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4941263d00>", "_build": "<function ActorCriticPolicy._build at 0x7f4941263d90>", "forward": "<function ActorCriticPolicy.forward at 0x7f4941263e20>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4941263eb0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4941263f40>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4941270040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f49412700d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4941270160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f49412701f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f49412644c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1706381054567620067, "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:": "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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"}, "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_test.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18c34264d5368daa2d2dd0f45481fba706703748ceb1bd64529cc3b6dd38f94b
+size 148048

ppo-LunarLander-v2_test/_stable_baselines3_version

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

ppo-LunarLander-v2_test/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 0x7f4941263b50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4941263be0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4941263c70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4941263d00>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4941263d90>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4941263e20>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4941263eb0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4941263f40>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4941270040>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f49412700d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4941270160>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f49412701f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f49412644c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1706381054567620067,
+    "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:": "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_test/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9b6f84c4abd716eb4e9e04995f6a57688b24f548e6972865dfda8243040bfbc
+size 88362

ppo-LunarLander-v2_test/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d62c632ac08ee709de69524e8c48c05dc3602d5a27f3b7bb53c88af3893c89f
+size 43762

ppo-LunarLander-v2_test/pytorch_variables.pth

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

ppo-LunarLander-v2_test/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": 279.7024276, "std_reward": 19.65821950256707, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-27T19:27:02.574058"}