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: 256.77 +/- 20.62
+      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 0x7db36cfd40e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7db36cfd4180>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7db36cfd4220>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7db36cfd42c0>", "_build": "<function ActorCriticPolicy._build at 0x7db36cfd4360>", "forward": "<function ActorCriticPolicy.forward at 0x7db36cfd4400>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7db36cfd44a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7db36cfd4540>", "_predict": "<function ActorCriticPolicy._predict at 0x7db36cfd45e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7db36cfd4680>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7db36cfd4720>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7db36cfd47c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7db36dfc2800>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1741685046682393274, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAE1yxj1p/iI/o7x0PWvHNr4xX108JT+JvAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.00044800000000000395, "_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": 3908, "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": 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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.11.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu124", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.1", "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 sha256:4b16704b5b70fd8761d04050a8be22cd88918e6c4a0a1da736ab46faeecbcedc
+size 147478

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 0x7db36cfd40e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7db36cfd4180>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7db36cfd4220>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7db36cfd42c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7db36cfd4360>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7db36cfd4400>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7db36cfd44a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7db36cfd4540>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7db36cfd45e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7db36cfd4680>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7db36cfd4720>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7db36cfd47c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7db36dfc2800>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1741685046682393274,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAE1yxj1p/iI/o7x0PWvHNr4xX108JT+JvAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.00044800000000000395,
+    "_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": 3908,
+    "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": 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:fda6b4f102bb63090cf2b01591b43d7c17439294970342912a768cc81790967d
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0435226705ccd36d9ec279a88904ba29f0b762c433308411723c0c0b906c9b7
+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: 3.11.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.5.1+cu124
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84c13a69d94c8546a7f6d79a598a0714004dd4a898f4c855a836bb088c74d67d
+size 183066

results.json

@@ -0,0 +1 @@
+{"mean_reward": 256.77201096827986, "std_reward": 20.61669579027736, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-03-11T09:56:29.711387"}