First commit of ppo tutorial

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: 266.53 +/- 18.86
+      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 0x7fbf602e95e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbf602e9670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbf602e9700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbf602e9790>", "_build": "<function ActorCriticPolicy._build at 0x7fbf602e9820>", "forward": "<function ActorCriticPolicy.forward at 0x7fbf602e98b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbf602e9940>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbf602e99d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbf602e9a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbf602e9af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbf602e9b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fbf6035edb0>"}, "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": 1670511378187192662, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "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": 266.5318093356965, "std_reward": 18.863963045629536, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T15:22:54.718937"}

unit1_ppo.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:01108220af0260bc2bcd915950cfc121afa0a8c1af7654034a39e4d8f8fe17d4
+size 146411

unit1_ppo/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

unit1_ppo/data

@@ -0,0 +1,91 @@
+{
+    "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 0x7fbf602e95e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbf602e9670>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbf602e9700>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbf602e9790>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbf602e9820>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbf602e98b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbf602e9940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbf602e99d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbf602e9a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbf602e9af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbf602e9b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fbf6035edb0>"
+    },
+    "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": 1670511378187192662,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

unit1_ppo/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ee89ff44c27da94b1bb31d4c11696f89b553a30b4c3f5901a398107ac8dddd7
+size 88057

unit1_ppo/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8eabc52313d67efd83f90fb2e818e429bfe7c20f9f92887336a07b17574ae28f
+size 43201

unit1_ppo/pytorch_variables.pth

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

unit1_ppo/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.15
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0