Today the earwig, tomorrow man?
Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Artificial intelligence and mobile robots
An Behavior-based Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Development of an Autonomous Quadruped Robot for Robot Entertainment
Autonomous Robots - Special issue on autonomous agents
Machine Learning
Evolving Fuzzy Logic Controllers for Sony Legged Robots
RoboCup 2001: Robot Soccer World Cup V
Integration of partially observable markov decision processes and reinforcement learning for simulated robot navigation
Evaluating the dynamics of agent-environment interaction
Evaluating the dynamics of agent-environment interaction
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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This paper presents a Q-learning approach to state-based planning of behaviour-based walking robots. The learning process consists of a teaching stage and an autonomous learning stage. During the teaching stage, the robot is instructed to operate in some interesting areas of the solution space to accumulate some prior knowledge. Then, the learning is switched to the autonomous learning stage to let the robot explore the solution space based on its prior knowledge. Experiments are conducted in the RoboCup domain and results show a good performance of the proposed method.