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: 255.11 +/- 35.43
+      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 0x7d08cb75e160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d08cb75e200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d08cb75e2a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d08cb75e340>", "_build": "<function ActorCriticPolicy._build at 0x7d08cb75e3e0>", "forward": "<function ActorCriticPolicy.forward at 0x7d08cb75e480>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d08cb75e520>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d08cb75e5c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d08cb75e660>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d08cb75e700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d08cb75e7a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d08cb75e840>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d08cbb71600>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1742941383390737520, "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:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="}, "_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": 337, "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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "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:": "gAWV1gIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjExL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUS4RDCPiAANgPEogKlEMAlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTEvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpRoAIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCF9lH2UKGgYjARmdW5jlIwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBmMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "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": "2.6.0+cu124", "GPU Enabled": "True", "Numpy": "2.0.2", "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 sha256:593ed373e57db44a93c7c891c9277bc0d5164c41722f1b0b989c187767c1014d
+size 148034

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 0x7d08cb75e160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d08cb75e200>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d08cb75e2a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d08cb75e340>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d08cb75e3e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d08cb75e480>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d08cb75e520>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d08cb75e5c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d08cb75e660>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d08cb75e700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d08cb75e7a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d08cb75e840>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d08cbb71600>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1742941383390737520,
+    "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:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="
+    },
+    "_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": 337,
+    "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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu",
+        "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 sha256:6fbf3cdc95b18108a92af3ee4207456421e4591daee3e3aa36f968ec21d45001
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ceb3c6cd9aaf180db30aa86df4337ee2c8e6af9154a09ed8dfb7d6652b868f7b
+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: 2.6.0+cu124
+- GPU Enabled: True
+- Numpy: 2.0.2
+- 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 sha256:6f7429f4a3e8766edf1d80282807ebdc5a802eb1512f6da73c169d05a3c93a55
+size 141864

results.json

@@ -0,0 +1 @@
+{"mean_reward": 255.1053632, "std_reward": 35.432910139976215, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-03-25T22:56:53.029510"}