Upload PPO LunarLander-v2 trained agent

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

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: 267.80 +/- 9.12
+      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 0x7e1fc4fecd60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e1fc4fece00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e1fc4fecea0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e1fc4fecf40>", "_build": "<function ActorCriticPolicy._build at 0x7e1fc4fecfe0>", "forward": "<function ActorCriticPolicy.forward at 0x7e1fc4fed080>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e1fc4fed120>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e1fc4fed1c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7e1fc4fed260>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e1fc4fed300>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e1fc4fed3a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e1fc4fed440>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e1fc4fe8a40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1744263091968250918, "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": 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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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-6.8.0-1021-aws-x86_64-with-glibc2.35 # 23~22.04.1-Ubuntu SMP Tue Dec 10 16:50:46 UTC 2024", "Python": "3.11.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.26.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:479bdacdd25936c8d7fd58f9643615e813e928b892d04b209c6c775331dddddb
+size 148189

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 0x7e1fc4fecd60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e1fc4fece00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e1fc4fecea0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e1fc4fecf40>",
+        "_build": "<function ActorCriticPolicy._build at 0x7e1fc4fecfe0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7e1fc4fed080>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e1fc4fed120>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e1fc4fed1c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7e1fc4fed260>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e1fc4fed300>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e1fc4fed3a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e1fc4fed440>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7e1fc4fe8a40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1744263091968250918,
+    "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": 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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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:d7671ac6c5225f97b4e822609380ad829ee1f5a4cf542e48930432943905bfc8
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:252a242c87cfaf2c154cc76894462aff851f4eb1a9a82e9d50708604fc95d970
+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-6.8.0-1021-aws-x86_64-with-glibc2.35 # 23~22.04.1-Ubuntu SMP Tue Dec 10 16:50:46 UTC 2024
+- Python: 3.11.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.5.1
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.26.2

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cba033b0369a3a21dc2d49f0cea3965ff17fdb794a9b3ea8efda9d1ada0cd812
+size 188332

results.json

@@ -0,0 +1 @@
+{"mean_reward": 267.7956473, "std_reward": 9.117625714786303, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-04-10T06:12:16.591620"}