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: 263.89 +/- 18.60
+      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 0x7e27b8c8a560>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e27b8c8a5f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e27b8c8a680>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e27b8c8a710>", "_build": "<function ActorCriticPolicy._build at 0x7e27b8c8a7a0>", "forward": "<function ActorCriticPolicy.forward at 0x7e27b8c8a830>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e27b8c8a8c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e27b8c8a950>", "_predict": "<function ActorCriticPolicy._predict at 0x7e27b8c8a9e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e27b8c8aa70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e27b8c8ab00>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e27b8c8ab90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e27b8c2f780>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": 1, "action_noise": null, "start_time": 1699117213567724429, "learning_rate": 0.003, "tensorboard_log": "tensorboard-logs/ppo-LunarLander-v2-stable-reward-penalize-time/LunarLander-v2", "_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:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 16, "n_steps": 1024, "gamma": 0.99, "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "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 sha256:5cf8d6dadabe4d8f911b1b01494b8a96f19c6322208cc92717d8f9e0eb916782
+size 148263

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 0x7e27b8c8a560>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e27b8c8a5f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e27b8c8a680>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e27b8c8a710>",
+        "_build": "<function ActorCriticPolicy._build at 0x7e27b8c8a7a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7e27b8c8a830>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e27b8c8a8c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e27b8c8a950>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7e27b8c8a9e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e27b8c8aa70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e27b8c8ab00>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e27b8c8ab90>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7e27b8c2f780>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": 1,
+    "action_noise": null,
+    "start_time": 1699117213567724429,
+    "learning_rate": 0.003,
+    "tensorboard_log": "tensorboard-logs/ppo-LunarLander-v2-stable-reward-penalize-time/LunarLander-v2",
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAHoDQz60s7Q+sE5pviCHir7WYKS88nazvAAAAAAAAAAAwHKcPU+JLbxmoE+9VY1zPYJhhz3wfhK9AACAPwAAgD96BzE+kFKRPtAxib7Sv56+XbTJvaIJAj0AAAAAAAAAANocyj38U3w+I5YOvgsSRL4JCrM64KnQvAAAAAAAAAAAgC8cPa9kNT9uWJa9Bujfvnhpsbw/OpC8AAAAAAAAAADaT7M9PbpguWv7KL3LRkM9QSi3u5edJb4AAIA/AACAP83n7z2jnDk/ErTWPKIUzr7ffMg9lURzvQAAAAAAAAAAZpyYPR49mT6mMku8e6KTvgRKDLwAx029AAAAAAAAAABmKsk7e6Kpusrwfjl8j240IFZzunRRkrgAAIA/AACAP6YXlL0V26Y/+qgvv8tmCb+0LKu6bTIQvgAAAAAAAAAA5kV+PRQol7qpgTuzrSeir2/vfTr+cMEzAACAPwAAgD/zbPI9XRGNPlEXlL2KmbC+oU37vJbTzLwAAAAAAAAAAGYejryPDmW6Sk5INwAqRTKz75Y7FRFrtgAAgD8AAIA/ZtnCPOyxm7nFP8w57UsLNVRQGDvTp/O4AACAPwAAgD8zwde8SNO2ujvw0zNJkTEwdm6COUWFrLMAAIA/AACAPxp0/D3gy8s+CXKVvnEMir7+C8m8jnsLvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_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:": "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",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "Generator(PCG64)"
+    },
+    "n_envs": 16,
+    "n_steps": 1024,
+    "gamma": 0.99,
+    "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:eefdd024902b14a02bf54646a489ec2478647a43c9b25bc1d0dc49e03564b40b
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:facdbca71f02cee68c0c105486244155a1b3c3ccc6c9deec20e907afb5022d73
+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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- 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": 263.8872381658603, "std_reward": 18.601058218153543, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-04T17:22:08.006607"}