This paper presents an adaptive routing algorithm, called Q-Agents, based on the integration of three learning strategies combined with some mechanisms to increase its speed of adaptation. The strategies were: Q-learning, dual reinforcement learning and learning based on ant colony behavior. The proposed algorithm is composed of a set of simple mobile agents that communicate indirectly between themselves and cooperate to find the best paths through the network. The agents select the routes in a distributed way and update information used in this task incrementally. Q-Agents has been applied to a telephone network from British Telecom and the mean percentage of lost calls by it was compared with the percentage of two algorithms based on ant colony behavior. The experiments comprised variations of the network traffic patterns, load level and topology and use of noise in information to select the routes. Q-Agents performed better than its competitors, presenting higher capacity of adaptation to the considered situations.
Telecommunications networks; routing; Q-learning; ant-based agents
