GA-Based Q-Learning to Develop Compact Control Table for Multiple Agents

Recently reinforcement learning has received much attention as a learning method (Sutton, 1988; Watkins & Dayan, 1992). It does not need a priori knowledge and has higher capability of reactive and adaptive behaviors. However there are some significant problems in applying it to real problems. Some of them are deep cost of learning and large size of actionstate space. The Q-learning (Watkins & Dayan, 1992), known as one of effective reinforcement learning, has difficulty in accomplishing learning tasks when the size of action-state space is large. Therefore the application of the usual Q-learning is restricted to simple tasks with the small action-state space. Due to the large action-state space, it is difficult to apply the Q-learning directly to real problems such as control problem for robots with many redundant degrees of freedom or multiple agents moving cooperatively one another. In order to cope with such difficulty of large action-state space, various structural and dividing algorithms of the action-state space were proposed (Holland, 1986; Svinin et al., 2001; Yamada et al., 2001). In the dividing algorithm, the state space is divided dynamically, however, the action space is fixed so that it is impossible to apply the algorithm to the task with a large action space. In the classifier system, “don’t care” attribute is introduced in order to create general rules. But, that causes partially observable problems. Furthermore, an ensemble system of general and special rules should be prepared in advance. Considering these points, Ito & Matsuno (2002) proposed a GA-based Q-learning method called “Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA).” In their algorithm, a genetic algorithm is employed to reconstruct an action-state space which is learned by Q-learning. That is, the size of the action-state space is reduced by the genetic algorithm in order to apply Q-learning to the learning process of that space. They applied their algorithm to a control problem of multi-legged robot which has many redundant degrees of freedom and a large action-state space. By applying their restriction method for the action-state space, they successfully obtained the control rules for a multi-legged robot by their QDSEGA. However, the way to apply a genetic algorithm in their approach seems so straightforward. Therefore we have proposed a crossover for QDSEGA (Murata & Yamaguchi, 2005; Murata & Yamaguchi, 2008). Through their computer simulations on a control problem of a multi-legged robot, they could make about 50% reduction of the number of generations to obtain a target state of the problem. Source: New Achievements in Evolutionary Computation, Book edited by: Peter Korosec, ISBN 978-953-307-053-7, pp. 318, February 2010, INTECH, Croatia, downloaded from SCIYO.COM