A Policy Adaptation Method for Implicit Multitask Reinforcement Learning
  Problems
Satoshi Yamamori Jun Morimoto
In dynamic motion generation tasks, including contact and collisions, small
changes in policy parameters can lead to extremely different returns. For
example, in soccer, the ball can fly in completely different directions with a
similar heading motion by slightly changing the hitting position or the force
applied to the ball or when the friction of the ball varies. However, it is
difficult to imagine that completely different skills are needed for heading a
ball in different directions. In this study, we proposed a multitask
reinforcement learning algorithm for adapting a policy to implicit changes in
goals or environments in a single motion category with different reward
functions or physical parameters of the environment. We evaluated the proposed
method on the ball heading task using a monopod robot model. The results showed
that the proposed method can adapt to implicit changes in the goal positions or
the coefficients of restitution of the ball, whereas the standard domain
randomization approach cannot cope with different task settings.