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: 272.87 +/- 18.96
+      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 0x7f846c5edab0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f846c5edb40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f846c5edbd0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f846c5edc60>", "_build": "<function ActorCriticPolicy._build at 0x7f846c5edcf0>", "forward": "<function ActorCriticPolicy.forward at 0x7f846c5edd80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f846c5ede10>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f846c5edea0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f846c5edf30>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f846c5edfc0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f846c5ee050>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f846c5ee0e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f846c5e6540>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682811799927058786, "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, "_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": 620, "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, "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": 10, "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "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:e74bb6f9115882682c703134846e46c2bab198336764d920c91e40874a9f4d1b
+size 147333

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

ppo-LunarLander-v2/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7f846c5edab0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f846c5edb40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f846c5edbd0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f846c5edc60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f846c5edcf0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f846c5edd80>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f846c5ede10>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f846c5edea0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f846c5edf30>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f846c5edfc0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f846c5ee050>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f846c5ee0e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f846c5e6540>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1682811799927058786,
+    "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,
+    "_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": 620,
+    "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,
+    "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": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "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:beb8f7e2c2e761ba69fd83e96249dbac58fb53c4b6e8bf6b55763241e16814ff
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87c28b15602ac322175346c9221153b371f6462c3e0a4d4f8176b21475441828
+size 43329

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: 3.10.11
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 272.86976788862796, "std_reward": 18.957039091615886, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-30T00:26:32.327575"}