Phe-Q: A Pheromone Based Q-Learning

摘要

Biological systems have often provided inspiration for the design of artificial systems. On such example of a natural system that has inspired researchers is the ant colony. In this paper an algorithm for multi-agent reinforcement learning, a modified Q-learning, is proposed. The algorithm is inspired by the natural behaviour of ants, which deposit pheromones in the environment to communicate. The benefit besides simulating ant behaviour in a colony is to design complex multi-agent systems. Complex behaviour can emerge from relatively simple interacting agents. The proposed Q-learning update equation includes a belief factor. The belief factor reflects the confidence the agent has in the pheromone detected in its environment. Agents communicate implicitly to co-operate in learning to solve a path-planning problem. The results indicate that combining synthetic pheromone with standard Q-learning speeds up the learning process. It will be shown that the agents can be biased towards a preferred solution by adjusting the pheromone deposit and evaporation rates.