Initial commit of PPO model for LunarLander-v2

LunarLander_PPO.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e30818171e8c6317037e0d6bec9aa088fdc05752c6821eb71bfe2a6297b52d11
+size 147335

LunarLander_PPO/_stable_baselines3_version

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

LunarLander_PPO/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 0x7f582325ad30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f582325adc0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f582325ae50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f582325aee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f582325af70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f582325e040>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f582325e0d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f582325e160>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f582325e1f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f582325e280>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f582325e310>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f582325e3a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5823254c90>"
+    },
+    "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": 1677012584006071904,
+    "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": 380,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

LunarLander_PPO/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b7dbc0f9acbf1e2c4062e73a80159525742fc09c08a2e0b905e208bba155fb55
+size 87929

LunarLander_PPO/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a992ade5a5911cd08a4227f98dff97208c83d958ca62246c67c79e86ddcce2b
+size 43393

LunarLander_PPO/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

LunarLander_PPO/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+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: 256.07 +/- 22.91
+      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 0x7f582325ad30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f582325adc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f582325ae50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f582325aee0>", "_build": "<function ActorCriticPolicy._build at 0x7f582325af70>", "forward": "<function ActorCriticPolicy.forward at 0x7f582325e040>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f582325e0d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f582325e160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f582325e1f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f582325e280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f582325e310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f582325e3a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5823254c90>"}, "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": 1677012584006071904, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAA3byz3h4Iu6wskONJ6jcC1IWLW6+5aaswAAgD8AAIA/Zs4rPY82f7r0STY6wfyktU9r1rqd8FK5AACAPwAAgD+ApSy+0iOYPGeRtbyMn6K9Tgr8vD/4rb0AAAAAAAAAAICWur328H667k54ty0Sk7KBUyg7TtGQNgAAgD8AAIA/kx8tvi7xmbzNdma6+UkmubiDBT4O6+A5AACAPwAAgD+A4zK+6bVhvPb/Zru9COi5m4a+PY3ApzoAAIA/AACAP7o1cL5q3pc/m4HWviD++b4z9tO+0yrwvQAAAAAAAAAABlxGPgo1J7shabC9U0teOnYo0rvCckA7AACAPwAAgD9zTV0+01JyP9KDoD4Ozu++VaVQPiUtE70AAAAAAAAAAA17Mj5p72u8ClBLuw9RzjmfBdG9NtKWOgAAgD8AAIA/k8ZWPtIF3Ty9va45fIFiOBrZcz4IIP+4AACAPwAAgD9my8083OesP04hWz7OEsG+x0XuPGNSzD0AAAAAAAAAAI7fpb4i7yk/RldXPT5V0r61xgO+O5LlPQAAAAAAAAAAwLmuPZJDrD42NQ69VYeKvqQSET2I2O+8AAAAAAAAAABm/LC98dijP+It5L4HKg2/o9HqvUUVdb4AAAAAAAAAAHMwKj4pUnO84tQhPHdJproiO929dvSFuwAAgD8AAIA/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": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 380, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 256.06955964165616, "std_reward": 22.914370368012932, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-21T21:40:51.116807"}