Upload PPO CartPole-v1 trained agent

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

CartPole-v1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9bc46828ffdbb5572813b387315e24e1123bb9d62ead2d975ef648e1004a537b
+size 141598

CartPole-v1/_stable_baselines3_version

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+1.5.0

CartPole-v1/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 0x7f99ef495b00>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f99ef495b90>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f99ef495c20>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f99ef495cb0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f99ef495d40>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f99ef495dd0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f99ef495e60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f99ef495ef0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f99ef495f80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f99ef49a050>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f99ef49a0e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f99ef4e0b10>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
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+        "shape": [
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+        ],
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+        "high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]",
+        "bounded_below": "[ True  True  True  True]",
+        "bounded_above": "[ True  True  True  True]",
+        "_np_random": "RandomState(MT19937)"
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
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+        "n": 2,
+        "shape": [],
+        "dtype": "int64",
+        "_np_random": "RandomState(MT19937)"
+    },
+    "n_envs": 1,
+    "num_timesteps": 500736,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1654527675.387251,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
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CartPole-v1/policy.optimizer.pth

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+size 79709

CartPole-v1/policy.pth

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+oid sha256:0fa2261ef761c1292a7a99e81f76dc2a5f5f3b188d3364cb9607ee5161ed26f0
+size 40641

CartPole-v1/pytorch_variables.pth

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+size 431

CartPole-v1/system_info.txt

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+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.17.3

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- CartPole-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 500.00 +/- 0.00
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CartPole-v1
+      type: CartPole-v1
+---
+
+# **PPO** Agent playing **CartPole-v1**
+This is a trained model of a **PPO** agent playing **CartPole-v1**
+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

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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 0x7f99ef495b00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f99ef495b90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f99ef495c20>", "_build_mlp_extractor": "<function 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+oid sha256:245e151cc5dbef0d0e18d464e9abd2d8b19921ef87f842ea5a3154d7b2db746f
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results.json

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