Deep Multi-Task Multi-Agent Reinforcement Learning With Knowledge Transfer

Despite the potential of Multi-Agent Reinforcement Learning (MARL) in addressing numerous complex tasks, training a single team of MARL agents to handle multiple diverse team tasks remains a challenge. In this paper, we introduce a novel Multi-task method based on Knowledge Transfer in cooperative MARL (MKT-MARL). By learning from task-specific teachers, our approach empowers a single team of agents to attain expert-level performance in multiple tasks. MKT-MARL utilizes a knowledge distillation algorithm specifically designed for the multi-agent architecture, which rapidly learns a team control policy incorporating common coordinated knowledge from the experience of task-specific teachers. Additionally, we enhance this training with teacher annealing, gradually shifting the model's learning from distillation towards environmental rewards. This enhancement helps the multi-task model surpass its single-task teachers. We extensively evaluate our algorithm using two commonly-used benchmarks: StarCraft II micro-management and multi-agent particle environment. The experimental results demonstrate that our algorithm outperforms both the single-task teachers and a jointly-trained team of agents. Extensive ablation experiments illustrate the effectiveness of the supervised knowledge transfer and the teacher annealing strategy.