In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Building upon ideas from preference-based bandit learning and posterior sampling in RL, we present Dueling Posterior Sampling (DPS), which employs preference-based posterior sampling to learn both the system dynamics and the underlying utility function that governs the user's preferences. Because preference feedback is provided on trajectories rather than individual state/action pairs, we develop a Bayesian approach to solving the credit assignment problem, translating user preferences to a posterior distribution over state/action reward models. We prove an asymptotic no-regret rate for DPS with a Bayesian logistic regression credit assignment model; to our knowledge, this is the first regret guarantee for preference-based RL. We also discuss possible avenues for extending this proof methodology to analyze other credit assignment models. Finally, we evaluate the approach empirically, showing competitive performance against existing baselines.

使用基于偏好的后验采样和贝叶斯方法解决了强化学习中的信用指派问题，提出了一种新的算法DUELING POSTERIOR SAMPLING（DPS），并且给出了第一个关于基于偏好的RL的后验保证率。

倾向性强化学习的竞争后验抽样