  Transfer Learning (TL) has shown great potential to accelerate Reinforcement
Learning (RL) by leveraging prior knowledge from past learned policies of
relevant tasks. Existing transfer approaches either explicitly computes the
similarity between tasks or select appropriate source policies to provide
guided explorations for the target task. However, how to directly optimize the
target policy by alternatively utilizing knowledge from appropriate source
policies without explicitly measuring the similarity is currently missing. In
this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL
by taking advantage of this idea. Our framework learns when and which source
policy is the best to reuse for the target policy and when to terminate it by
modeling multi-policy transfer as the option learning problem. PTF can be
easily combined with existing deep RL approaches. Experimental results show it
significantly accelerates the learning process and surpasses state-of-the-art
policy transfer methods in terms of learning efficiency and final performance
in both discrete and continuous action spaces.
