個々の目的を持つ自律分散型マルチエージェントにおける相関関係の学習

抄録

Multi-agent reinforcement learning (MARL) is a framework to make multiple agents (e.g., robots) in the same environment learn their policies simultaneously using reinforcement learning. In the conventional MARL, although decentralization is essential for feasible learning, rewards for the agents have been given from a centralized system (named as top-down MARL). To achieve the completely distributed autonomous systems, we tackle a new paradigm named bottom-up MARL, where the agents get respective rewards. The bottom-up MARL requires to share the respective rewards for creating orderly group behaviors, and therefore, methods to do so were investigated through simulations. We found that the orderly group behaviors could be created by considering the relationship between the agents.