  Dynamic platforms that operate over manyunique terrain conditions typically
require multiple controllers.To transition safely between controllers, there
must be anoverlap of states between adjacent controllers. We developa novel
method for training Setup Policies that bridge thetrajectories between
pre-trained Deep Reinforcement Learning(DRL) policies. We demonstrate our
method with a simulatedbiped traversing a difficult jump terrain, where a
single policyfails to learn the task, and switching between pre-trainedpolicies
without Setup Policies also fails. We perform anablation of key components of
our system, and show thatour method outperforms others that learn transition
policies.We demonstrate our method with several difficult and diverseterrain
types, and show that we can use Setup Policies as partof a modular control
suite to successfully traverse a sequence ofcomplex terrains. We show that
using Setup Policies improvesthe success rate for traversing a single difficult
jump terrain(from 1.5%success rate without Setup Policies to 82%), and
asequence of various terrains (from 6.5%without Setup Policiesto 29.1%).
