Efficient reinforcement learning
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
A Study of Some Properties of Ant-Q
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the efficient implementation biologic reinforcement learning using eligibility traces
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A cooperation online reinforcement learning approach in ant-q
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Efficient ant reinforcement learning using replacing eligibility traces
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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When agent chooses some action and does state transition in present state in reinforcement learning, it is important subject to decide how will reward for conduct that agent chooses. In this paper, by new meta heuristic method to solve hard combinatorial optimization problems, we introduce Ant-Q learning method that has been proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search, and suggest ant reinforcement learning model using TD-error(ARLM-TDE). We could know through an experiment that proposed reinforcement learning method converges faster to optimal solution than original ACS and Ant-Q.