7 research outputs found
Dueling Posterior Sampling for Preference-Based Reinforcement Learning
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
Preference-Based Learning for Exoskeleton Gait Optimization
This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users
Preference-Based Learning for Exoskeleton Gait Optimization
This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
A crucial task in decision-making problems is reward engineering. It is
common in practice that no obvious choice of reward function exists. Thus, a
popular approach is to introduce human feedback during training and leverage
such feedback to learn a reward function. Among all policy learning methods
that use human feedback, preference-based methods have demonstrated substantial
success in recent empirical applications such as InstructGPT. In this work, we
develop a theory that provably shows the benefits of preference-based methods
in offline contextual bandits. In particular, we improve the modeling and
suboptimality analysis for running policy learning methods on human-scored
samples directly. Then, we compare it with the suboptimality guarantees of
preference-based methods and show that preference-based methods enjoy lower
suboptimality