First DRL model

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: 256.15 +/- 20.41
+      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 0x7f6b9fea53a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6b9fea5430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6b9fea54c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6b9fea5550>", "_build": "<function ActorCriticPolicy._build at 0x7f6b9fea55e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f6b9fea5670>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6b9fea5700>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6b9fea5790>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6b9fea5820>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6b9fea58b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6b9fea5940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6b9fea24e0>"}, "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": 1672957816811452449, "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": 252, "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-glibc2.27 #1 SMP Sat Dec 10 16:00:40 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"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:458a4118f8e4acc8710e1b2eaace3e8897b8aa1daeb54af774486c817420bb50
+size 147210

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 0x7f6b9fea53a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6b9fea5430>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6b9fea54c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6b9fea5550>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f6b9fea55e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f6b9fea5670>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6b9fea5700>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f6b9fea5790>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6b9fea5820>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6b9fea58b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6b9fea5940>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f6b9fea24e0>"
+    },
+    "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": 1672957816811452449,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAO0MW7702RG9a+8fO1rQ2TkdToA+2hBjugAAgD8AAAAAhtE9vqXvlj5CnKo97/J/vn1MHr2wi9a7AAAAAAAAAAAaQKq9sSHyPgE4Dj4Et6O+DD66Pb4rq7sAAAAAAAAAAHpbA76Mq5Q+u87ePYHghb6kwBe98lWjvQAAAAAAAAAAc4OUva4/nzlefNK6wgH7taatxbuAU/05AACAPwAAgD8tKVc+7hCxvDusIjv4n4C5SiUevtt9SroAAIA/AACAPyC5Vz7c1Tk9u1jevSmzR77qC1g95lH3OwAAAAAAAAAAmlk9PK6Ll7ooW2K5CedqtHPU6jqNlII4AACAPwAAgD8GpjO+nC1lvHInfLpOQYy4pT3CPUTanTkAAIA/AACAPxqUPD666so+UEsjvo+3R76p2b28GiEfvQAAAAAAAAAA+n+LPuobRD99h1U+MqfVvhKOlz4WtAK+AAAAAAAAAABm3xm9rrGAuk6NLrfruvOxmF5wunZ3SzYAAIA/AACAP+aN0z24nte3Yy7quuJsrbWO0qu7hroJOgAAAAAAAIA/zafHvSm9Nbz5mwM8kzsfPcq1Sj38+ZS4AACAPwAAgD/N+lc8EQnnPp6ko70PKky+cRb2vAJJub0AAAAAAAAAAFqCyj320HG6U5POuhWkFTXwADG7EsTsOQAAgD8AAIA/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": 252,
+    "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "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 sha256:629fe25a92f29a2767c623a880e7d8779a300fd404e4bbd37453276928d06180
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8285a236c6f3cca1f85c7f8386a76acba7b73ffd1372f4b1db2dcf17f1851043
+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-5.10.147+-x86_64-with-glibc2.27 #1 SMP Sat Dec 10 16:00:40 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": 256.1514454548624, "std_reward": 20.41385542556987, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-05T22:52:46.706531"}