Modular reinforcement learning is an approach to resolve the curse of dimensionality problem in traditional reinforcement learning. We design and implement a modular reinforcement learning algorithm, which is based on three major components: Markov decision process decomposition, module training, and global action selection. We define and formalize module class and module instance concepts in decomposition step. Under our framework of decomposition, we train each modules efficiently using SARSA($\lambda$) algorithm. Then we design, implement, test, and compare three action selection algorithms based on different heuristics: Module Combination, Module Selection, and Module Voting. For last two algorithms, we propose a method to calculate module weights efficiently, by using standard deviation of Q-values of each module. We show that Module Combination and Module Voting algorithms produce satisfactory performance in our test domain. / text

<p>This thesis presents new methods for finding optimal and near-optimal constant weight binary codes with distance d and weight w such that d=2(w-1). These methods have led to the discovery of a number of new codes which are being submitted for publication. Improvements in methods for generating lexicographic codes are also discussed, with suggestions for further research in this area.</p>

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