4 papers
Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
Ethan Blaser, Jiuqi Wang, Shangtong Zhang
The average reward is a fundamental performance metric in reinforcement learning (RL) focusing on the long-run performance of an agent. Differential temporal difference (TD) learni…
Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise
Ethan Blaser, Shangtong Zhang
Stochastic approximation is a powerful class of algorithms with celebrated success. However, a large body of previous analysis focuses on stochastic approximations driven by contra…
Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
Jiuqi Wang, Ethan Blaser, Hadi Daneshmand +1
Traditionally, reinforcement learning (RL) agents learn to solve new tasks by updating their neural network parameters through interactions with the task environment. However, rece…
A Survey of In-Context Reinforcement Learning
Amir Moeini, Jiuqi Wang, Jacob Beck +4
Reinforcement learning (RL) agents typically optimize their policies by performing expensive backward passes to update their network parameters. However, some agents can solve new…