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cs.LG2026
CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning
Mahyar Alinejad, Yue Wang, George Atia
Transfer learning promises to reduce the high sample complexity of deep reinforcement learning (RL), yet existing methods struggle with domain shift between source and target envir…
cs.LG2025
RLAF: Reinforcement Learning from Automaton Feedback
Mahyar Alinejad, Alvaro Velasquez, Yue Wang +1
Reinforcement Learning (RL) in environments with complex, history-dependent reward structures poses significant challenges for traditional methods. In this work, we introduce a nov…