Reinforcement Learning
Multi-turn Reinforcement Learning from Preference Human Feedback
In the tabular setting, we present a novel mirror-descent-based policy optimization algorithm for the general multi-turn preference-based RL problem, and prove its convergence to Nash equilibrium.
Verified Safe Reinforcement Learning for Neural Network Dynamic Models
Learning reliably safe autonomous control is one of the core problems in trustworthy autonomy.
State-free Reinforcement Learning
Reinforcement learning (RL) studies the problem where an agent interacts with an unknown environment to optimize cumulative rewards/losses [Sutton and Barto, 2018].
Reinforcement Learning with L TL and ฯ-Regular Objectives via Optimality-Preserving Translation to Average Rewards Xuan-Bach Le
In this work, we study the relationship between these objectives.
Focus On What Matters: Separated Models For Visual-Based RL Generalization
Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (Separated Models for Generalization), a novel approach that exploits image reconstruction for generalization.
Offline Behavior Distillation
Inspired by dataset distillation (DD) [Wang et al., 2018, Zhao et al., (Corollary 1). Extensive experiments on nine datasets of D4RL benchmark [Fu et al., 2020] with multiple environments and data qualities illustrate that our Av-PBC remarkably promotes the OBD performance, Moreover, Av-PBC has a significant convergence speed and requires only a quarter of distillation steps compared to DBC and PBC.