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: 249.06 +/- 26.76
+      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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f45022c3040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f45022c30d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f45022c3160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f45022c31f0>", "_build": "<function ActorCriticPolicy._build at 0x7f45022c3280>", "forward": "<function ActorCriticPolicy.forward at 0x7f45022c3310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f45022c33a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f45022c3430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f45022c34c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f45022c3550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f45022c35e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f45022be480>"}, "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": 1672916874265843882, "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, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+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:c08473a888cace03dbe6344078088e6cfde7a451729b3aa6adb7cf7ec2eed5c0
+size 147210

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f45022c3040>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f45022c30d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f45022c3160>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f45022c31f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f45022c3280>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f45022c3310>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f45022c33a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f45022c3430>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f45022c34c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f45022c3550>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f45022c35e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f45022be480>"
+    },
+    "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": 1672916874265843882,
+    "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 sha256:3bb2a7b418aa51f357a50fc91a81499c532275770f6294ae727f67ca9d002100
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:edf57f290e246690fffb62b3344cac543ac74eed382c532407f21185239d7599
+size 43201

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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 249.05632869161232, "std_reward": 26.761665286753704, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-05T12:02:24.570429"}