init

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: 251.42 +/- 19.77
+      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 0x7f5e6c123dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5e6c123e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5e6c123ee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5e6c123f70>", "_build": "<function ActorCriticPolicy._build at 0x7f5e6c127040>", "forward": "<function ActorCriticPolicy.forward at 0x7f5e6c1270d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5e6c127160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5e6c1271f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5e6c127280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5e6c127310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5e6c1273a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5e6c127430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5e6c1a2390>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676551040131356925, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAM3y87xcaz264GFMO0QIlTQ20CM6BvtxugAAgD8AAIA/GkUGvcPBPbpqujS6M7eftG82P7sLEVQ5AACAPwAAgD8ATo48kUl2P12oVL3NOK++ozgAO0IzQr0AAAAAAAAAAAAhbb2u64i6M8q2umkqtbUhOBU7MpDUOQAAgD8AAIA/c8JtPnRUXz8x0g29y9iBvs2lDz5Adw2+AAAAAAAAAABGS0I+P3aXP6siyz57QLW+fVeyPkauJT4AAAAAAAAAAE1Ilb24rrW5VwFBOMvrNzPhSAs7hqBjtwAAgD8AAIA/TYYlvVzXKLpVmWa7vCFbNbAtoDujqMu0AACAPwAAgD/NfFW9j9oLuikvFbk0W9y11Q/Du6VcRTUAAIA/AACAP2auj7v29Be6VPKDOx48nrXQFoq6hMOZugAAgD8AAIA/moK8Pb9YAD/qDFu+VT6evtCMwr0KNaE8AAAAAAAAAACAWBK921fdPSC5BL0sRW2+/tIGvTIpEz0AAAAAAAAAAAALtbxIp6y6n3nMO8r/ZDduvSs6GropNgAAgD8AAIA/MyWBvEgHsLqXlpq6osJGtp+HL7qO/LA5AACAPwAAgD9Nm4A9XHNWutqgljf+tocyD9y/uB67sbYAAIA/AACAP5oK4b3MV04+CAxHPSw5Qb4Aylu947BfPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-lunarlander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8f085a582ed2b82ad2efc626611938444dec050062cf2ddd29379d588382a28
+size 147420

ppo-lunarlander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-lunarlander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f5e6c123dc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5e6c123e50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5e6c123ee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5e6c123f70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5e6c127040>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5e6c1270d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5e6c127160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5e6c1271f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5e6c127280>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5e6c127310>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5e6c1273a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5e6c127430>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5e6c1a2390>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676551040131356925,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAM3y87xcaz264GFMO0QIlTQ20CM6BvtxugAAgD8AAIA/GkUGvcPBPbpqujS6M7eftG82P7sLEVQ5AACAPwAAgD8ATo48kUl2P12oVL3NOK++ozgAO0IzQr0AAAAAAAAAAAAhbb2u64i6M8q2umkqtbUhOBU7MpDUOQAAgD8AAIA/c8JtPnRUXz8x0g29y9iBvs2lDz5Adw2+AAAAAAAAAABGS0I+P3aXP6siyz57QLW+fVeyPkauJT4AAAAAAAAAAE1Ilb24rrW5VwFBOMvrNzPhSAs7hqBjtwAAgD8AAIA/TYYlvVzXKLpVmWa7vCFbNbAtoDujqMu0AACAPwAAgD/NfFW9j9oLuikvFbk0W9y11Q/Du6VcRTUAAIA/AACAP2auj7v29Be6VPKDOx48nrXQFoq6hMOZugAAgD8AAIA/moK8Pb9YAD/qDFu+VT6evtCMwr0KNaE8AAAAAAAAAACAWBK921fdPSC5BL0sRW2+/tIGvTIpEz0AAAAAAAAAAAALtbxIp6y6n3nMO8r/ZDduvSs6GropNgAAgD8AAIA/MyWBvEgHsLqXlpq6osJGtp+HL7qO/LA5AACAPwAAgD9Nm4A9XHNWutqgljf+tocyD9y/uB67sbYAAIA/AACAP5oK4b3MV04+CAxHPSw5Qb4Aylu947BfPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_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,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "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
+}

ppo-lunarlander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:807a08c114f61a40aafc0c14b279f896f593e5294e95690dd4fab19ace383692
+size 87929

ppo-lunarlander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b78ccd56858fa78db5b630cf14c6a1c3acf548ac9612759bc513befe6dee561
+size 43393

ppo-lunarlander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-lunarlander-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 251.42236261054305, "std_reward": 19.77415640185432, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-16T13:07:38.500477"}