Upload of my PPO LunarLander-v2 trained agent

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 291.20 +/- 18.45
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+# **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 0x7f40003661f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4000366280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4000366310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f40003663a0>", "_build": "<function ActorCriticPolicy._build at 0x7f4000366430>", "forward": "<function ActorCriticPolicy.forward at 0x7f40003664c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4000366550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f40003665e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4000366670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4000366700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4000366790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4000366820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f4000364b00>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVpQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoC0sIhZSMAUOUdJRSlIwEaGlnaJRoEyiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoC0sIhZRoFnSUUpSMDWJvdW5kZWRfYmVsb3eUaBMolggAAAAAAAAAAAAAAAAAAACUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYIAAAAAAAAAAAAAAAAAAAAlGgiSwiFlGgWdJRSlIwKX25wX3JhbmRvbZROdWIu", "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 3014656, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677089529150791485, "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": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 736, "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-5.4.0-122-generic-x86_64-with-glibc2.31 # 138~18.04.1-Ubuntu SMP Fri Jun 24 14:14:03 UTC 2022", "Python": "3.9.13", "Stable-Baselines3": "1.8.0a2", "PyTorch": "1.12.0+cu116", "GPU Enabled": "True", "Numpy": "1.23.1", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da206feea1c19fdc377313c8863bad276d868ae33331fcfb20b3068efb2f6ae3
+size 147346

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0a2

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 0x7f40003661f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4000366280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4000366310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f40003663a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4000366430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f40003664c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4000366550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f40003665e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4000366670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4000366700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4000366790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4000366820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f4000364b00>"
+    },
+    "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 3014656,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677089529150791485,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAOCuNz4RVBo/WgXcvZczD79cPYg+2mA2vgAAAAAAAAAAzXzMOs88frxu9Sq8TE8tPYvQi72NiXA8AACAPwAAgD8A3pc8qgGzP9c3SD7TjSu+iIodPNiHcT0AAAAAAAAAAG0rDr7X65E/ammNvg29Fb/z16i+tm4JvgAAAAAAAAAApgMuvo9EeD8mzAK/bfYUvxm+kb7+w8i+AAAAAAAAAAAziGM9j6I0uu9jP7men9a0zaqbO2axXTgAAIA/AACAP3Ousj3PAFw/tBu2PTWpCb/3sz8+IyccPQAAAAAAAAAAmvOavKfg1D4UNyI9XN7pvp0DlTzCYDY9AAAAAAAAAAAAiXc9RduJPEJUs77ll5++ABVwvrL+WT4AAIA/AAAAAM25hz3Wp8E+fp0Xvu7w375F5H49HgcovgAAAAAAAAAAmmZWPewWUT5ymBC+lY3Yvv72gjpGmdq9AAAAAAAAAADN6C28aNvMPnqekD0SiNS+/qO/PHrGDj0AAAAAAAAAADNpFD5fIc0+pl85vgEI0L69UPo9LUTzvQAAAAAAAAAAoOoJPhTPkz5m8qG+dovOvnwWKT0Bt0u+AAAAAAAAAAAA8ba8FylhP5A2Kj1R2CG/z4zDvfXfgz0AAAAAAAAAAGbQYrwUeIm6ppYyONr/PTMKv++5Qv5OtwAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.004885333333333408,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 736,
+    "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 sha256:587333da52b205b07d32e25e125a35283a6e283f0e6263ee43ca8db7bbd13047
+size 87993

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83cc4f507b22d15c46ca2c5f81c427be597abaace4c2dd582326b63bc914ac9b
+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-5.4.0-122-generic-x86_64-with-glibc2.31 # 138~18.04.1-Ubuntu SMP Fri Jun 24 14:14:03 UTC 2022
+- Python: 3.9.13
+- Stable-Baselines3: 1.8.0a2
+- PyTorch: 1.12.0+cu116
+- GPU Enabled: True
+- Numpy: 1.23.1
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 291.19962733146315, "std_reward": 18.45235303130195, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-23T12:43:28.425998"}