27 citations · 86 across the 44 of their papers we have counts for
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cs.AI2026
Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL
Ruiming Liang, Yi Zhong, Yizhen Yuan +6
Modern large language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases. As a result, multi-reward reinfo…
cs.AI2023★ 1 cited
A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
Jianxiong Li, Shichao Lin, Tianyu Shi +5
The optimization of traffic signal control (TSC) is critical for an efficient transportation system. In recent years, reinforcement learning (RL) techniques have emerged as a popul…
cs.AI2021★ 5 cited
Model-Based Offline Planning with Trajectory Pruning
Xianyuan Zhan, Xiangyu Zhu, Haoran Xu
The recent offline reinforcement learning (RL) studies have achieved much progress to make RL usable in real-world systems by learning policies from pre-collected datasets without…