Update barkour notebook.

PiperOrigin-RevId: 610906071
Change-Id: Ie67b86888e195bbd8a8e879547e65fbc2c864534
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Baruch Tabanpour
2024-02-27 16:08:22 -08:00
committed by Copybara-Service
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# Google Barkour v0 Joystick Policy
## Overview
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.
`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.
<p float="left">
<img src="assets/joystick.gif" width="400">
</p>
# Google Barkour v0
## MJCF Instructions
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"source": [
"## Train Humanoid Policy\n",
"\n",
"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."
"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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"source": [
"## Train Policy\n",
"\n",
"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."
"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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},
"outputs": [],
"source": [
"HTML(html.render(eval_env.sys, rollout))"
"HTML(html.render(eval_env.sys.replace(dt=eval_env.dt), rollout))"
]
}
],