ConserWeightive Behavioral Cloning for Reliable Offline Reinforcement Learning. (arXiv:2210.05158v1 [cs.LG])

The goal of offline reinforcement learning (RL) is to learn near-optimal
policies from static logged datasets, thus sidestepping expensive online
interactions. Behavioral cloning (BC) provides a straightforward solution to
offline RL by mimicking offline trajectories via supervised learning. Recent
advances (Chen et al., 2021; Janner et al., 2021; Emmons et al., 2021) have
shown that by conditioning on desired future returns, BC can perform
competitively to their value-based counterparts, while enjoying much more
simplicity and training stability. However, the distribution …

arxiv cloning offline reinforcement reinforcement learning