A Policy Adaptation Method for Implicit Multitask Reinforcement Learning Problems

arXiv:2308.16471v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv cs.lg cs.ro dynamic example fly parameters policy reinforcement reinforcement learning returns small soccer tasks type