Dueling Posterior Sampling for Preference-Based Reinforcement Learning

TL;DR

This paper introduces DUELING POSTERIOR SAMPLING (DPS), a novel Bayesian approach for preference-based reinforcement learning that learns system dynamics and utility functions from trajectory preferences, providing the first regret guarantees in this setting.

Contribution

The paper presents DPS, a new Bayesian posterior sampling method for preference-based RL, with theoretical regret guarantees and empirical evaluation demonstrating competitive performance.

Findings

Proves asymptotic Bayesian no-regret rate for DPS.

Develops a Bayesian credit assignment approach for trajectory preferences.

Shows DPS performs competitively against existing methods.

Abstract

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 preference feedback. As preference feedback is provided on trajectories rather than individual state-action pairs, we develop a Bayesian approach for the credit assignment problem, translating preferences to a posterior distribution over state-action reward models. We prove an asymptotic Bayesian no-regret rate for DPS with a Bayesian linear regression credit assignment model. This is the first regret guarantee for preference-based RL to our knowledge. We also discuss possible avenues for extending the proof methodology to other credit assignment models. Finally, we evaluate the approach empirically, showing competitive performance against existing baselines.