We propose for the first time two reinforcement learning algorithms with function approximation for average cost adaptive control of traffic lights. One of these algorithms is a version of Q-learning with function approximation while the other is a policy gradient actor-critic algorithm that incorporates multi-timescale stochastic approximation. We show performance comparisons on various network settings of these algorithms with a range of fixed timing algorithms, as well as a Q-learning algorithm with full state representation that we also implement. We observe that whereas (as expected) on a two-junction corridor, the full state representation algorithm shows the best results, this algorithm is not implementable on larger road networks. The algorithm PG-AC-TLC that we propose is seen to show the best overall performance.


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    Title :

    Reinforcement learning with average cost for adaptive control of traffic lights at intersections


    Contributors:


    Publication date :

    2011-10-01


    Size :

    597887 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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