  Dynamic platforms that operate over many unique terrain conditions typically
require many behaviours. To transition safely, there must be an overlap of
states between adjacent controllers. We develop a novel method for training
setup policies that bridge the trajectories between pre-trained Deep
Reinforcement Learning (DRL) policies. We demonstrate our method with a
simulated biped traversing a difficult jump terrain, where a single policy
fails to learn the task, and switching between pre-trained policies without
setup policies also fails. We perform an ablation of key components of our
system, and show that our method outperforms others that learn transition
policies. We demonstrate our method with several difficult and diverse terrain
types, and show that we can use setup policies as part of a modular control
suite to successfully traverse a sequence of complex terrains. We show that
using setup policies improves the success rate for traversing a single
difficult jump terrain (from 51.3% success rate with the best comparative
method to 82.2%), and traversing a random sequence of difficult obstacles (from
1.9% without setup policies to 71.2%).
