Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Comparison of Parallel Messy Genetic Algorithm Data Distribution Strategies
Proceedings of the 5th International Conference on Genetic Algorithms
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees
ICML '05 Proceedings of the 22nd international conference on Machine learning
Efficient reinforcement learning using recursive least-squares methods
Journal of Artificial Intelligence Research
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We contribute two new algorithms in this paper called PImGA and PIrlEA respectively in which we construct populations online in each iteration. Every iteration process in these two algorithms does not like the normal EA and GA in which they employ the inefficient value iteration method in general, instead of, in this paper, we employ the efficient policy iteration as the computation method for searching optimal control actions or policies. Meanwhile,these algorithms also do not like general EA and GA for selection operator to get a optimal policy, instead of we make the Agent learning a good or elite policy from its parents population. The resulted policy will be as one of elements of the next population. Because this policy is obtained by taking optimal reinforcement learning algorithm and greedy policy, the new population always can be constructed by applying better policies than its parents, that is to say, the child or offspring will inherit parents' good or elite abilities. Intuitively, for a finite problem, the resulted population from simulation will accommodate the near optimal policies after a number of iterations. Our experiments show that the algorithms can work well.