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One of the main issues of autonomous robots is how to implement a system with learning capability to acquire both varieties of knowledge and behaviors through the interaction between the robot and the environment during its lifetime. There have been a lot of different works on learning approaches for robots to acquire behaviors based on the methods such as reinforcement learning, genetic algorithms, and so on. Especially, reinforcement learning has recently been receiving increased attention as a method for behavior learning with little or no a priori knowledge and higher capability of reactive and adaptive behaviors. However, a simple and straightforward application of reinforcement learning methods to real robot tasks is considerably difficult due to its almost endless exploration of which time easily scales up exponentially with the size of the state/action spaces, which seems almost impossible from a practical viewpoint. One of the potential solutions might be application of so-called “mixture of experts” proposed by Jacobs and Jordan (Jacobs & Jordan, 1991), in which a set of expert modules learn and one gating system weights the output of the each expert module for the final system output. This idea is very general and has a wide range of applications. However, we have to consider the following two issues to apply it to the real robot tasks: Task decomposition: how to find a set of simple behaviors and assign each of them to a learning module or an expert in order to achieve the given initial task. Usually, human designer carefully decomposes the long time-scale task into a sequence of simple behaviors such that the one short time-scale subtask can be accomplished by one learning module. Abstraction of state and/or action spaces for scaling up: the original “mixture of experts” consists of experts and a gate for expert selection. Therefore, no more abstraction beyond the gating module. In order to cope with complicated real robot tasks, more abstraction of the state and/or action spaces is necessary. Connell and Mahadevan (Connell & Mahadevan, 1993) decomposed the whole behavior into sub-behaviors each of which can be independently learned. Morimoto and Doya (Morimoto & Doya 1998) applied a hierarchical reinforcement learning method by which an appropriate sequence of subgoals for the task is learned in the upper level while behaviors to achieve the subgoals are acquired in the lower level. Hasegawa and Fukuda (Hasegawa & Fukuda, 1999, 2001) proposed a hierarchical behavior controller, which consists of three types of

Multi-Layered Learning System for Real Robot Behavior Acquisition