Multi-Agent Deep Reinforcement Learning with Human Strategies
This work addresses the problem of enhancing exploration in multi-agent deep reinforcement learning for researchers and practitioners, though it appears incremental as it builds on existing methods by adding human strategies.
The paper tackles the limited exploration capacity of deep reinforcement learning agents by integrating human strategies into multi-agent systems, resulting in significant performance improvement in a custom simulation environment called Multiple Tank Defence.
Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increase the exploration capacity of multiple deep reinforcement learning agents. We also report the development of our own multi-agent environment called Multiple Tank Defence to simulate the proposed approach. The results show the significant performance improvement of multiple agents that have learned cooperatively with human strategies. This implies that there is a critical need for human intellect teamed with machines to solve complex problems. In addition, the success of this simulation indicates that our multi-agent environment can be used as a testbed platform to develop and validate other multi-agent control algorithms.