Push LunarLander-v2 model

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: 247.00 +/- 29.15
+      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 0x7fb8012534c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb801253550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb8012535e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb801253670>", "_build": "<function ActorCriticPolicy._build at 0x7fb801253700>", "forward": "<function ActorCriticPolicy.forward at 0x7fb801253790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb801253820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb8012538b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb801253940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb8012539d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb801253a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb801253af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fb8012572c0>"}, "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": 1680727876139060611, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_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, "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+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:087fcb3dc469462aab92e9dd43bda1be0d2488c57db4bf38b770494211e64283
+size 147424

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 0x7fb8012534c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb801253550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb8012535e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb801253670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb801253700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb801253790>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb801253820>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb8012538b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb801253940>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb8012539d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb801253a60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb801253af0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fb8012572c0>"
+    },
+    "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": 1680727876139060611,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_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,
+    "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 sha256:809c3c0ab72b8dd4d562f0f25a7432311e578fa42e910814bda25fbb453279db
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cea91ef2f203d2f52785f1de3d94e853baff03bc2a4b3748d212201e0781bf7
+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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 247.00421692500436, "std_reward": 29.14936836166967, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-05T21:24:41.372036"}