  Recent studies on online reinforcement learning (RL) have demonstrated the
advantages of learning multiple behaviors from a single task, as in the case of
few-shot adaptation to a new environment. Although this approach is expected to
yield similar benefits in offline RL, appropriate methods for learning multiple
solutions have not been fully investigated in previous studies. In this study,
we therefore addressed the problem of finding multiple solutions from a single
task in offline RL. We propose algorithms that can learn multiple solutions in
offline RL, and empirically investigate their performance. Our experimental
results show that the proposed algorithm learns multiple qualitatively and
quantitatively distinctive solutions in offline RL.
