Jinyin Bai

You are already at the latest version Reward functions determine what reinforcement learning agents ultimately optimize, yet reward design for complex tasks has traditionally relied on extensive domain expertise and iterative engineering. Recent large language models and vision–language foundation models have introduced new mechanisms for interpreting task intent, synthesizing reward programs, evaluating states and trajectories, and refining rewards through policy feedback.

Abstract Multi-agent reinforcement learning (MARL) has achieved substantial progress in cooperative decision-making, but learning remains difficult in environments with sparse rewards, long decision horizons, and strong inter-agent coupling. Existing methods usually optimize low-level policies directly from numerical observations, which can limit sample efficiency and make it difficult to incorporate structured strategic guidance.

You are already at the latest version Post-disaster emergency communication recovery is not merely a link-repair task but a high-level planning problem constrained by service priorities, inter-object dependencies, resource budgets, and time windows.