Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A simple tree search method for playing Ms. Pac-Man
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Monte-Carlo tree search for the physical travelling salesman problem
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
| Hi-index | 0.00 |
In real-time games, agents have limited time to respond to environmental cues. This requires either a policy defined up-front or, if one has access to a generative model, a very efficient rolling horizon search. In this paper, different search techniques are compared in a simple, yet interesting, real-time game known as the Physical Travelling Salesman Problem (PTSP).We introduce a rolling horizon version of a simple evolutionary algorithm that handles macro-actions and compare it against Monte Carlo Tree Search (MCTS), an approach known to perform well in practice, as well as random search. The experimental setup employs a variety of settings for both the action space of the agent as well as the algorithms used. We show that MCTS is able to handle very fine-grained searches whereas evolution performs better as we move to coarser-grained actions; the choice of algorithm becomes irrelevant if the actions are even more coarse-grained. We conclude that evolutionary algorithms can be a viable and competitive alternative to MCTS.