Upload PPO LunarLander-v2 trained 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: 218.13 +/- 65.57
+      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 0x7dda78ff9990>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dda78ff9a20>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dda78ff9ab0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dda78ff9b40>", "_build": "<function ActorCriticPolicy._build at 0x7dda78ff9bd0>", "forward": "<function ActorCriticPolicy.forward at 0x7dda78ff9c60>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7dda78ff9cf0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dda78ff9d80>", "_predict": "<function ActorCriticPolicy._predict at 0x7dda78ff9e10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dda78ff9ea0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dda78ff9f30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dda78ff9fc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7dda79196980>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1712463218901806654, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAObAOr1qmYQ/tXmgu+5wbL7cNpU7uQaquwAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "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": "Generator(PCG64)"}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 1, "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": "2.2.1+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:fb96fb5c784ad28ac98e516f435d574e308fe701a42b3060b22b3fb3090c785b
+size 147990

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 0x7dda78ff9990>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dda78ff9a20>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dda78ff9ab0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dda78ff9b40>",
+        "_build": "<function ActorCriticPolicy._build at 0x7dda78ff9bd0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7dda78ff9c60>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7dda78ff9cf0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dda78ff9d80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7dda78ff9e10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dda78ff9ea0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dda78ff9f30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dda78ff9fc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7dda79196980>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1712463218901806654,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAObAOr1qmYQ/tXmgu+5wbL7cNpU7uQaquwAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "_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": 3908,
+    "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": "Generator(PCG64)"
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "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",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "Generator(PCG64)"
+    },
+    "n_envs": 1,
+    "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 sha256:1c2c5b7551e376d36cdf5c4a708047ff8530868482d5030ce4ac0f1e752f10c2
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4bdd89a491ff37645bb622911e548f262096d8b82dd84d54c080a70620b1867b
+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: 2.2.1+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": 218.13438065277757, "std_reward": 65.56997039610059, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-07T05:08:49.658658"}