How to Fool Your Supervisor

Abstract

Reinforcement learning is being broadly studied as a solution for many real-world problems. Reinforcement learning algorithms outperform humans in games such as Go and chess. On the other hand, reinforcement learning still can not achieve human-level in most real-world tasks. When reinforcement learning agents perform similar to humans, they will replace humans in many areas. Many real-world tasks need cooperation, and multi-agent reinforcement learning appears as a solution. However, multi-agent reinforcement learning algorithms are not yet being applied because of the challenge of high-dimensional data, computational complexity, and credit assignment. Meanwhile, people can manage to adapt to a new group without understanding the global goal of the group he or she belongs to. This is because people moderately imitate senior workers. Our work aims to test if the imitation strategy can be applied to a reinforcement learning agent. To validate the proposed methodology, we design a problem named Convoy problem and evaluate our new algorithm on the convoy problem.