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: 290.63 +/- 13.15
+      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 0x7876ca6584c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7876ca658550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7876ca6585e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7876ca658670>", "_build": "<function ActorCriticPolicy._build at 0x7876ca658700>", "forward": "<function ActorCriticPolicy.forward at 0x7876ca658790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7876ca658820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7876ca6588b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7876ca658940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7876ca6589d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7876ca658a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7876ca658af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7876ca65c180>"}, "verbose": true, "policy_kwargs": {}, "num_timesteps": 1001472, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1712689199988758886, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAOBDYT4NOKE/aAbkPrRvFL+5IdQ+XU1zPgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.0014719999999999178, "_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": 5870, "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": 1, "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-6.1.58+-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 sha256:88c5003720d39d2d2baf634c2b9de36732bb7cf76b15fe9f8011c45dccfa1544
+size 147299

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 0x7876ca6584c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7876ca658550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7876ca6585e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7876ca658670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7876ca658700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7876ca658790>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7876ca658820>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7876ca6588b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7876ca658940>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7876ca6589d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7876ca658a60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7876ca658af0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7876ca65c180>"
+    },
+    "verbose": true,
+    "policy_kwargs": {},
+    "num_timesteps": 1001472,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1712689199988758886,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAOBDYT4NOKE/aAbkPrRvFL+5IdQ+XU1zPgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.0014719999999999178,
+    "_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": 5870,
+    "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": 1,
+    "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 sha256:4dac67588557033b4bfa1d0702d3d6772acc66212a11231420a9935a04fcebc2
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9bcecbdf0ac0449777c9d58c8a07015fffba559955afb832339b43ab397113f
+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": 290.62986179999996, "std_reward": 13.145046702638224, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-09T19:49:15.616916"}