Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach

NeurIPSSpotlight2025

Authors
Chenbei Lu, Zaiwei Chen, Tongxin Li, Chenye Wu, Adam Wierman
Venue
NeurIPS 2025
Track
Spotlight

TL;DR

Traditional reinforcement learning (RL) assumes the agents make decisions based on Markov decision processes (MDPs) with one-step transition models…

Opening excerpt from the authors’ abstract. source

Read the paper

Topics

reinforcement learning agent llm

← All NeurIPS 2025 Spotlight papers · Browse the whole archive