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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 different options, finds a common policy in a common environment. Information about action goodness is transmitted using traces over state-action pairs. MDQL extends this idea to 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 continuous functions is also given. Successful results of MDQL on several test-bed problems suggested in the literature are described.