Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
This addresses the need for diverse solution strategies in offline RL, which is incremental as it extends online RL concepts to the offline setting.
The paper tackled the problem of learning multiple distinct behaviors from a single task in offline reinforcement learning, proposing algorithms that empirically demonstrate the ability to find qualitatively and quantitatively distinctive solutions.
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.