Model trained using Optuna for hyperparameter tuning

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: 302.36 +/- 15.24
+      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 0x7fa2e4f6a790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa2e4f6a820>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa2e4f6a8b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa2e4f6a940>", "_build": "<function ActorCriticPolicy._build at 0x7fa2e4f6a9d0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa2e4f6aa60>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa2e4f6aaf0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa2e4f6ab80>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa2e4f6ac10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa2e4f6aca0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa2e4f6ad30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa2e4f6adc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa2e4f67360>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678125256029095466, "learning_rate": 0.0001, "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": 3748, "n_steps": 512, "gamma": 0.997, "gae_lambda": 0.95, "ent_coef": 6.38e-06, "vf_coef": 0.5, "max_grad_norm": 0.31, "batch_size": 32, "n_epochs": 5, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "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": "1.22.4", "Gym": "0.21.0"}}

lander_optuna_2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37e9c936826a9b3359b5a3d821b9c32bbebae69cea753aaab4995d2f8e3c0121
+size 147427

lander_optuna_2/_stable_baselines3_version

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

lander_optuna_2/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 0x7fa2e4f6a790>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa2e4f6a820>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa2e4f6a8b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa2e4f6a940>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa2e4f6a9d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa2e4f6aa60>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa2e4f6aaf0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa2e4f6ab80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa2e4f6ac10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa2e4f6aca0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa2e4f6ad30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa2e4f6adc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fa2e4f67360>"
+    },
+    "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": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678125256029095466,
+    "learning_rate": 0.0001,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/Gjbi6xxDLYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_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": 3748,
+    "n_steps": 512,
+    "gamma": 0.997,
+    "gae_lambda": 0.95,
+    "ent_coef": 6.38e-06,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.31,
+    "batch_size": 32,
+    "n_epochs": 5,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

lander_optuna_2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53dcc1deed4aa3e6aece5561d47fd24c49006c595de1486d93b832a109aa26a2
+size 88057

lander_optuna_2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3054c649ce71f559fae6d302cb492ca6df455a0dd3ddbb6b62c9cb8c840edcfa
+size 43393

lander_optuna_2/pytorch_variables.pth

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

lander_optuna_2/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: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 302.3566343278907, "std_reward": 15.242004149179634, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-06T18:05:17.000919"}