Update barkour notebook.
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# Google Barkour v0 Joystick Policy
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## Overview
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This folder contains a training script for a flat-terrain joystick policy for the [Barkour v0 Quadruped](https://ai.googleblog.com/2023/05/barkour-benchmarking-animal-level.html) which demonstrates sim2real transfer.
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`barkour_joystick.py` contains the environment definition, while the [colab](https://colab.research.google.com/github/google/brax/blob/main/experimental/barkour_v0/barkour_v0_joystick.ipynb) shows how to train the policy.
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<p float="left">
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<img src="assets/joystick.gif" width="400">
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</p>
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# Google Barkour v0
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## MJCF Instructions
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"source": [
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"## Train Humanoid Policy\n",
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"\n",
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"Let's now train a policy with PPO to make the Humanoid run forwards. Training takes about 8-9 minutes on a Tesla A100 GPU."
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"Let's now train a policy with PPO to make the Humanoid run forwards. Training takes about 6 minutes on a Tesla A100 GPU."
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]
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"source": [
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"## Train Policy\n",
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"\n",
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"To train a policy with domain randomization, we pass in the domain randomization function into the brax train function; brax will call the domain randomization function when rolling out episodes. Training the quadruped takes 8-9 minutes on a Tesla A100 GPU."
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"To train a policy with domain randomization, we pass in the domain randomization function into the brax train function; brax will call the domain randomization function when rolling out episodes. Training the quadruped takes 6 minutes on a Tesla A100 GPU."
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"HTML(html.render(eval_env.sys, rollout))"
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"HTML(html.render(eval_env.sys.replace(dt=eval_env.dt), rollout))"
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]
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}
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],
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