Semi-Offline Reinforcement Learning for Optimized Text Generation
Changyu Chen Xiting Wang Yiqiao Jin Victor Ye Dong Li Dong Jie Cao Yi Liu Rui Yan
In reinforcement learning (RL), there are two major settings for interacting
with the environment: online and offline. Online methods explore the
environment at significant time cost, and offline methods efficiently obtain
reward signals by sacrificing exploration capability. We propose semi-offline
RL, a novel paradigm that smoothly transits from offline to online settings,
balances exploration capability and training cost, and provides a theoretical
foundation for comparing different RL settings. Based on the semi-offline
formulation, we present the RL setting that is optimal in terms of optimization
cost, asymptotic error, and overfitting error bound. Extensive experiments show
that our semi-offline approach is efficient and yields comparable or often
better performance compared with state-of-the-art methods.