Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Osaka University "Trackies 2001"
RoboCup 2001: Robot Soccer World Cup V
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In order to obtain the feasible solution in the realistic learning time, a layer architecture is often introduced. This paper proposes a behavior selection mechanism with activation/termination constraints. In our method, each behavior has three components: policy, activation constraints, and termination constraints. A policy is a function mapping the sensor information to motor commands. Activation constraints reduce the number of situations where correspondingp olicy is executable, and termination constraints contribute to extract meaningful behavior sequences, each of which can be regarded as one action regardless of its duration. We apply the genetic algorithm to obtain the switching function to select the appropriate behavior accordingto the situation. As an example, a simplified soccer game is given to show the validity of the proposed method. Simulation results and real robots experiments are shown, and a discussion is given.