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: 278.04 +/- 19.74
+      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 0x7ff687bb5120>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff687bb51b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff687bb5240>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff687bb52d0>", "_build": "<function ActorCriticPolicy._build at 0x7ff687bb5360>", "forward": "<function ActorCriticPolicy.forward at 0x7ff687bb53f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff687bb5480>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff687bb5510>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff687bb55a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff687bb5630>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff687bb56c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff687bb5750>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff687d43c00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 4300000, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1711606498593470630, "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": 0.1414784, "_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": 1048, "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, "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:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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 sha256:812b9dd61ebf23cda4649a4626380c06ba798f063b9a464c7783504ca04ea928
+size 148125

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 0x7ff687bb5120>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff687bb51b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff687bb5240>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff687bb52d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff687bb5360>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff687bb53f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff687bb5480>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff687bb5510>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff687bb55a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff687bb5630>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff687bb56c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff687bb5750>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ff687d43c00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 4300000,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1711606498593470630,
+    "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": 0.1414784,
+    "_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": 1048,
+    "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,
+    "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:": "gAWV/QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgLjAJpOJSJiIeUUpQoSwNoD05OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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 sha256:9c63be60ee4bb3a5c941226f6d7f86e726fc7a72d0b18470c7704915ba1377c2
+size 88490

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d0ad41bc981978ae6d4446bcc1c562643482e494d0a2f24b030fd3056f28562
+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": 278.0434870516217, "std_reward": 19.736820834620104, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-28T07:23:49.171814"}