Adaptability Preserving Domain Decomposition for Stabilizing Sim2Real Reinforcement Learning
Haichuan Gao,Zhile Yang,Xin Su,Tian Tan,Feng Chen,Haichuan Gao,Zhile Yang,Xin Su,Tian Tan,Feng Chen
In sim-to-real transfer of Reinforcement Learning (RL) policies for robot tasks, Domain Randomization (DR) is a widely used technique for improving adaptability. However, in DR there is a conflict between adaptability and training stability, and heavy DR tends to result in instability or even failure in training. To relieve this conflict, we propose a new algorithm named Domain Decomposition (DD) ...
