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: 268.85 +/- 27.36
+      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 0x7d6d0009a5c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d6d0009a660>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d6d0009a700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d6d0009a7a0>", "_build": "<function ActorCriticPolicy._build at 0x7d6d0009a840>", "forward": "<function ActorCriticPolicy.forward at 0x7d6d0009a8e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d6d0009a980>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d6d0009aa20>", "_predict": "<function ActorCriticPolicy._predict at 0x7d6d0009aac0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d6d0009ab60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d6d0009ac00>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d6d0009aca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d6d00026b80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1738577187151467320, "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-6.1.85+-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:fcfb228c1627a9f4eda3804121679065b252ed21d2bdf30aaafef8b0719df9ee
+size 148040

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 0x7d6d0009a5c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d6d0009a660>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d6d0009a700>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d6d0009a7a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d6d0009a840>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d6d0009a8e0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d6d0009a980>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d6d0009aa20>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d6d0009aac0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d6d0009ab60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d6d0009ac00>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d6d0009aca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d6d00026b80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1738577187151467320,
+    "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:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=",
+        "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:": "gAWV1gIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjExL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUS4RDCPiAANgPEogKlEMAlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTEvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpRoAIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCF9lH2UKGgYjARmdW5jlIwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBmMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPzOpKjBVMmGFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18511d2100b57224060f629cb80c5321235943b90834480cd2a3cb53a219ab06
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ffe2281d14ba44a8cfbda0f0ee91edb00f5cb6c5b7dd764e9627ef37d0094a77
+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:abf5814e17a658a3c95cfb99306f1bd6ec26dec6364dd7dac853c2302aa874ed
+size 175887

results.json

@@ -0,0 +1 @@
+{"mean_reward": 268.8468338, "std_reward": 27.358197738483863, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-02-03T10:37:04.134545"}