  Legged robots often use separate control policiesthat are highly engineered
for traversing difficult terrain suchas stairs, gaps, and steps, where
switching between policies isonly possible when the robot is in a region that
is commonto adjacent controllers. Deep Reinforcement Learning (DRL)is a
promising alternative to hand-crafted control design,though typically requires
the full set of test conditions to beknown before training. DRL policies can
result in complex(often unrealistic) behaviours that have few or no
overlappingregions between adjacent policies, making it difficult to
switchbehaviours. In this work we develop multiple DRL policieswith Curriculum
Learning (CL), each that can traverse asingle respective terrain condition,
while ensuring an overlapbetween policies. We then train a network for each
destinationpolicy that estimates the likelihood of successfully switchingfrom
any other policy. We evaluate our switching methodon a previously unseen
combination of terrain artifacts andshow that it performs better than heuristic
methods. Whileour method is trained on individual terrain types, it
performscomparably to a Deep Q Network trained on the full set ofterrain
conditions. This approach allows the development ofseparate policies in
constrained conditions with embedded priorknowledge about each behaviour, that
is scalable to any numberof behaviours, and prepares DRL methods for
applications inthe real world
