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cs.LG2025
Reward Models in Deep Reinforcement Learning: A Survey
Rui Yu, Shenghua Wan, Yucen Wang +4
In reinforcement learning (RL), agents continually interact with the environment and use the feedback to refine their behavior. To guide policy optimization, reward models are intr…
cs.RO2025
Unleashing Humanoid Reaching Potential via Real-world-Ready Skill Space
Zhikai Zhang, Chao Chen, Han Xue +6
Humans possess a large reachable space in the 3D world, enabling interaction with objects at varying heights and distances. However, realizing such large-space reaching on humanoid…
cs.LG2025
Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning
Chen-Xiao Gao, Chenyang Wu, Mingjun Cao +3
Behavior regularization, which constrains the policy to stay close to some behavior policy, is widely used in offline reinforcement learning (RL) to manage the risk of hazardous ex…