Curriculum-guided decentralized deep Q-network for multi-agent pathfinding under intermittent obstacles
Multi-agent pathfinding (MAPF) requires multiple agents to reach assigned goals while avoiding vertex, edge, and intermittent-obstacle conflicts in a shared workspace. Learning-based MAPF is attractive for online decision-making, but fully decentralized reinforcement learning remains vulnerable to sparse rewards, non-s...