3 papers
cs.LG2025
Recursive Reward Aggregation
Yuting Tang, Yivan Zhang, Johannes Ackermann +3
In reinforcement learning (RL), aligning agent behavior with specific objectives typically requires careful design of the reward function, which can be challenging when the desired…
cs.LG2024
Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning
Yuting Tang, Xin-Qiang Cai, Jing-Cheng Pang +3
Reinforcement Learning (RL) empowers agents to acquire various skills by learning from reward signals. Unfortunately, designing high-quality instance-level rewards often demands si…
cs.LG2024
Reinforcement Learning from Bagged Reward
Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding +3
In Reinforcement Learning (RL), it is commonly assumed that an immediate reward signal is generated for each action taken by the agent, helping the agent maximize cumulative reward…