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: 252.12 +/- 75.21
+      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 0x7e266495f2e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e266495f370>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e266495f400>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e266495f490>", "_build": "<function ActorCriticPolicy._build at 0x7e266495f520>", "forward": "<function ActorCriticPolicy.forward at 0x7e266495f5b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e266495f640>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e266495f6d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7e266495f760>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e266495f7f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e266495f880>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e266495f910>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e2664b06600>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1726505316661228779, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAACW5bwT0a8/TTb+vaqftb4KDjO9R2A1vQAAAAAAAAAAwJg7vmFGu7zjKwE6yZKAOJ0SMj5ukDa5AACAPwAAgD9myR49o4hXP0nYmj2iPAC/iP/KPIL3nToAAAAAAAAAABpnQz38VnE9QuG/uxbiKb4cmAi8Fmh9PAAAAAAAAAAA7X14PlzTFj3KMxK8+gdLPJtIzT62tXI8AACAPwAAgD/NH5u8/I6zP3Jm7L67YgS+eH8tPHLFrbsAAAAAAAAAABreMj6oT5W8G+RKu04Snjmg8QG+qXKIOgAAgD8AAIA/PftQvkzzpz4/r6M+qo+qvnLInzwUKYI9AAAAAAAAAAANgJi9wEvePqJjgj7js6m+x/YQPNWjzDwAAAAAAAAAANoUhD4qiAu9S3hsuMy+gjbU/XW+Ty+iNwAAgD8AAIA/mqcUvvYDODvGjVg+jYG8vUqeCL6URSI/AACAPwAAAAAzxJK9jaO4PlStuz3lJJ6+GKMCvDAVqTwAAAAAAAAAAGaV171OcsY9PkWQPgrnc75iOqk9lor7PAAAAAAAAAAAAOOLPOBQhD+4mFA8JWYLv9Cg57rYkQ29AAAAAAAAAAAz8zQ+VIiTvKVC1DujQ1i63xgEvm3eK7sAAIA/AACAP/YQi77cnGS8nf2SOze0jTl9g8M9dbejugAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": 449, "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": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.4.0+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "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:d48469c751b99b6b5a6936e38fcc6e39d42fb4ea0d7b0ebfe4c3f991297f7da1
+size 147995

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 0x7e266495f2e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e266495f370>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e266495f400>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e266495f490>",
+        "_build": "<function ActorCriticPolicy._build at 0x7e266495f520>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7e266495f5b0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e266495f640>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e266495f6d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7e266495f760>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e266495f7f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e266495f880>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e266495f910>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7e2664b06600>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1726505316661228779,
+    "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": 449,
+    "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": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "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 sha256:522edb68f6ffc6d25adeff87175304004426944b952eda79961ae0d2c87a7591
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52593a48717c3ed09f42db1a3ed85b57f820c100b7f9909541c3c804d57cfd18
+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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.4.0+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 252.1162575, "std_reward": 75.21346983203043, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-09-16T17:14:34.328677"}