Upload LunarLander-v2 trained agent from Deep RL Course tutorial

PPO-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bac7852916f09ee8234e6fd3dc5f36ecc702240657b7264d9c58a0057ef2eb4b
+size 147127

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 0x7f44c54a2f70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f44c54a7040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f44c54a70d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f44c54a7160>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f44c54a71f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f44c54a7280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f44c54a7310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f44c54a73a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f44c54a7430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f44c54a74c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f44c54a7550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f44c54a41e0>"
+    },
+    "verbose": 0,
+    "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": 1671132963429533722,
+    "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": 310,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0,
+    "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
+}

PPO-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0700bfe7ba4ab9536441e43660b75aa4d140cc0b47879bb9537be38e0868bcf0
+size 87929

PPO-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:915caad45a6d86fe151edcca5b9407a0909f533c2ec056fc7420e8bee1c84f3b
+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

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: 269.28 +/- 26.68
+      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 0x7f44c54a2f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f44c54a7040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f44c54a70d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f44c54a7160>", "_build": "<function ActorCriticPolicy._build at 0x7f44c54a71f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f44c54a7280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f44c54a7310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f44c54a73a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f44c54a7430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f44c54a74c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f44c54a7550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f44c54a41e0>"}, "verbose": 0, "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": 1671132963429533722, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAFMnLL7urJy8+DJvuzVuD7rR6ws+5vHpOgAAgD8AAIA/sz63PY/yDrpkoCK+OjqLPG6ML7stVSw9AAAAAAAAgD/t8xE+uDTYu5I5wzrtjQ25MYEkvbN/5bkAAIA/AACAP4A/GD40wnU+SURCvk16j75768K81/WavQAAAAAAAAAAUxQmPsMUDLzwYf06m4K2uHNYYr2cXh26AACAPwAAgD8mIwA+vNSjP3aM3T4fotW+HT9BPs6BKT4AAAAAAAAAAOZlFL3DXWW6Q3wZM+CIH7BLWiU7m5K1swAAgD8AAIA/sxpWPeG6orj2Gx01UcyFL87uMjx+T2K0AACAPwAAgD+asPw8BZIjPqKu471TI5i+FmHbvEJMZLwAAAAAAAAAADOyJL2jzGw/QhnKvefBF7+/SnK9qrmGvQAAAAAAAAAAQI37vTL7nT9xHaK+mkIvv8DJA74V7Dm9AAAAAAAAAABm8Hi9KXBAuhwWG73ySyQ0QGpguR3tYbMAAIA/AACAPzpxAz64XuS5tl2AvrtuBr0Ibro9vhQsvgAAgD8AAIA/M3fNPHcNTD7GSsU9iklxvqqvMD0B5q69AAAAAAAAAAAmQxc+z0MavD4CGbs8lw059DWAvawXRjoAAIA/AACAP03IQz20R/E+it4GPMmG374kvcM6mGG1OgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": 310, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0, "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-5.10.133+-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": 269.2767387514664, "std_reward": 26.677943583204488, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-15T20:10:09.759551"}