first commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: MLpolicy
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 241.20 +/- 32.56
+      name: mean_reward
+      verified: false
+---
+
+# **MLpolicy** Agent playing **LunarLander-v2**
+This is a trained model of a **MLpolicy** 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 0x7f11da6869d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f11da686a60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f11da686af0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f11da686b80>", "_build": "<function ActorCriticPolicy._build at 0x7f11da686c10>", "forward": "<function ActorCriticPolicy.forward at 0x7f11da686ca0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f11da686d30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f11da686dc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f11da686e50>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f11da686ee0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f11da686f70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f11da68f040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f11da68ccc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681758672120586669, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAM0RhrwU5Ju6pDA/OsDEPjXvCpm6Q5RcuQAAgD8AAIA/2vmlvcGUED/FIno7H/B+vvzQPL3Wp7q8AAAAAAAAAAAa6SK9CkdauXXjRTuzDzM1gSEvOnagZ7oAAIA/AACAPzMPzju4loW5bdtlOFemgjOw94i3ob6FtwAAgD8AAIA/ZjCQvXFdAblCQlk7eyY2N4d8gLu6uSI2AACAPwAAAACArGq94QyEulqIUjxyGjezyMtJO4uxUrMAAIA/AACAP2Z51TzhuJ66nji6OrX/7jULNx65MNPUuQAAgD8AAIA/ABuXvBRglbq664U5b0+FNHrwnzq87Zq4AACAPwAAgD+aL/49rlWgumrtdLme2JU0Ko7Yuq9KjjgAAIA/AACAPwAIgryuhZG6PuBLO203WzhE4a861WTkuAAAgD8AAIA/msLFPTQVoD0ItuK9WeZqvvWN/bwAAWO8AAAAAAAAAABNZhU94eieujwKOzavSF8x4hOjOpNZZrUAAIA/AACAP5IojL7/MBk/e0ciPvdAor6skTS9mJlmPQAAAAAAAAAA8+/GvcPRSLpi7165W0A8thjFmLq+pIM4AACAPwAAAAAA3sE9hWu1uYjuhzeEbt0yRIX/OqYvobYAAIA/AACAP1NHcj4tR1I/e6AFvgWTg74p1zk8IP6ivQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-1e6-1024.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca55976c7773eaac23b55b2f48ab90ff4544c108ce6b7462b0ced20cf14b17fd
+size 147391

ppo-LunarLander-1e6-1024/_stable_baselines3_version

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

ppo-LunarLander-1e6-1024/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7f11da6869d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f11da686a60>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f11da686af0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f11da686b80>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f11da686c10>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f11da686ca0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f11da686d30>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f11da686dc0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f11da686e50>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f11da686ee0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f11da686f70>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f11da68f040>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f11da68ccc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681758672120586669,
+    "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,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "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,
+    "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-1e6-1024/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:200d828c3b47b36848ad7963bd27f40ebeabb008747399a3dcd96b88e4608e9a
+size 87929

ppo-LunarLander-1e6-1024/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69f5cd2d8d72070eac1006b448433bb7015f984f74ba8e968cb4aeaf33166221
+size 43329

ppo-LunarLander-1e6-1024/pytorch_variables.pth

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

ppo-LunarLander-1e6-1024/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: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 241.20423173954023, "std_reward": 32.559499032590566, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-17T19:54:37.707241"}