Minimalist mobile robotics: a colony-style architecture for an artificial creature
Minimalist mobile robotics: a colony-style architecture for an artificial creature
Active perception and reinforcement learning
Proceedings of the seventh international conference (1990) on Machine learning
Proceedings of the seventh international conference (1990) on Machine learning
Learning in embedded systems
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence
Acquisition of Movement Pattern by Q-Learning in Peristaltic Crawling Robot
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
Learning the behavior model of a robot
Autonomous Robots
A combined reactive and reinforcement learning controller for an autonomous tracked vehicle
Robotics and Autonomous Systems
Vector-valued function estimation by grammatical evolution for autonomous robot control
Information Sciences: an International Journal
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This paper describes a general approach for automatically programming a behavior-based robot. New behaviors are learned by trial and error using a performance feedback function as reinforcement. Two algorithms for behavior learning are described that combine techniques for propagating reinforcement values temporally across actions and spatially across states. A behavior-based robot called OBELIX (see Figure 1) is described that learns several component behaviors in an example task involving pushing boxes. An experimental study using the robot suggests two conclusions. One, the learning techniques are able to learn the individual behaviors, sometimes outperforming a hand-coded program. Two, using a behavior-based architecture is better than using a monolithic architecture for learning the box pushing task.