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cs.LG2026
Subspace Inference Enables Efficient Active Reward Learning from Preferences
Yutai Zhou, Erdem Bıyık
Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach for learning reward models from human preferences, making active learnin…
cs.LG2021★ 1 cited
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning
Sheng Li, Yutai Zhou, Ross Allen +1
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication wh…