First Commit On Lunar Lander Trained Agent

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: 267.31 +/- 24.90
+      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 0x7feebcaf9ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7feebcaf9f70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7feebcafc040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7feebcafc0d0>", "_build": "<function ActorCriticPolicy._build at 0x7feebcafc160>", "forward": "<function ActorCriticPolicy.forward at 0x7feebcafc1f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7feebcafc280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7feebcafc310>", "_predict": "<function ActorCriticPolicy._predict at 0x7feebcafc3a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7feebcafc430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7feebcafc4c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7feebcafc550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7feebcafe140>"}, "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": 1678613797419232971, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "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:c95da259f44da2fac87733f8360b84a5a4fbb166763e858f216e8c9133b39874
+size 147425

ppo-LunarLander-v2/_stable_baselines3_version

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

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 0x7feebcaf9ee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7feebcaf9f70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7feebcafc040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7feebcafc0d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7feebcafc160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7feebcafc1f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7feebcafc280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7feebcafc310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7feebcafc3a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7feebcafc430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7feebcafc4c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7feebcafc550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7feebcafe140>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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": 1678613797419232971,
+    "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 sha256:249d38973957d190dddf33d0ec5cb4f93ed9de974580aff31446e8d2914386f2
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc9e20a8b3d0177fb8bf704020b9b05e8bae82574996bcd087643bcc707eab9e
+size 43393

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: 3.9.16
+- 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": 267.3127069387244, "std_reward": 24.896592118875137, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-12T10:10:05.968336"}