A fundamental challenge in interactive learning and decision making, ranging from bandit problems to reinforcement learning, is to provide sample-efficient, adaptive learning …
Y Wang, Q Liu, C Jin - Advances in Neural Information …, 2023 - proceedings.neurips.cc
Abstract Reinforcement learning from Human Feedback (RLHF) learns from preference signals, while standard Reinforcement Learning (RL) directly learns from reward signals …
A Agarwal, S Kakade… - Advances in neural …, 2020 - proceedings.neurips.cc
In order to deal with the curse of dimensionality in reinforcement learning (RL), it is common practice to make parametric assumptions where values or policies are functions of some low …
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually complex environments. BYOL-Explore learns the world …
L Shi, G Li, Y Wei, Y Chen… - Advances in Neural …, 2024 - proceedings.neurips.cc
This paper investigates model robustness in reinforcement learning (RL) via the framework of distributionally robust Markov decision processes (RMDPs). Despite recent efforts, the …
Q Liu, T Yu, Y Bai, C Jin - International Conference on …, 2021 - proceedings.mlr.press
Abstract Model-based algorithms—algorithms that explore the environment through building and utilizing an estimated model—are widely used in reinforcement learning practice and …
We show that computing approximate stationary Markov coarse correlated equilibria (CCE) in general-sum stochastic games is PPAD-hard, even when there are two players, the game …
Abstract Model-based reinforcement learning (RL), which finds an optimal policy using an empirical model, has long been recognized as one of the cornerstones of RL. It is especially …
Coverage conditions--which assert that the data logging distribution adequately covers the state space--play a fundamental role in determining the sample complexity of offline …