Fuzzy Sarsa: An approach to linear function approximation in reinforcement learning
نویسنده
چکیده
This paper investigates two different approaches to learning using an agent electronic marketplace as test bed. The types of learning considered in this paper include the temporal difference (TD) learning algorithm Sarsa, and two new fuzzified versions of this algorithm, FQ Sarsa and Fuzzy Sarsa. We implement the three learning algorithms in an agent test bed in order to determine their usefulness in the context of an electronic marketplace. We present the results of various tests demonstrating that the Fuzzy Sarsa algorithm, while having the smallest state space, is also the more effective method of learning.
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