A New Distributed Reinforcement Learning Algorithm for MultipleObjective Optimization

This paper describes a new algorithm, called MDQL, for the solution of multiple objective optimization problems. MDQL is based on a new distributed Q-learning algorithm, called DQL, which is also introduced in this paper. In DQL a family of independent agents, exploring diierent options, nds a common policy in a common environment. Information about action goodness is transmitted using traces over state-action pairs. MDQL extends this idea for multiple objectives, assigning a family of agents for each objective involved. A non-dominant criterion is used to construct Pareto fronts and by delaying adjustments on the rewards MDQL achieves better distributions of solutions. Furthermore , an extension for applying reinforcement learning to continuos functions is also given. Successful results of MDQL on several test-bed problems suggested in the literature are described.