Efficient reinforcement learning
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Study of Some Properties of Ant-Q
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Multiagent reinforcement learning algorithm using temporal difference error
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
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
| Hi-index | 0.00 |
The eligibility trace is one of the basic mechanisms in reinforcement learning to handle delayed reward. In this paper, we have used meta-heuristic method to solve hard combinatorial optimization problems. Our proposed solution introduce Ant-Q learning method to solve Traveling Salesman Problem (TSP). The approach is based on population that use positive feedback as well as greedy search and suggest ant reinforcement learning algorithms using eligibility traces which is called replace-trace methods(Ant-TD(λ)). Although replacing traces are only slightly, they can produce a significant improvement in learning rate. We could know through an experiment that proposed reinforcement learning method converges faster to optimal solution than ACS and Ant-Q.