Thijs Wensveen

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Since biological systems have the ability to efficiently reuse previous experiences to change their behavioral strategies to avoid enemies or find food, the number of required samples from real environments to improve behavioral policy is greatly reduced. Even for real robotic systems, it is desirable to use only a limited number of samples from real(More)
In this study, we show that a movement policy can be improved efficiently using the previous experiences of a real robot. Reinforcement Learning (RL) is becoming a popular approach to acquire a nonlinear optimal policy through trial and error. However, it is considered very difficult to apply RL to real robot control since it usually requires many learning(More)
Optimistic planning (OP) is a promising approach for receding-horizon optimal control of general nonlinear systems. This generality comes however at large computational costs, which so far have prevented the application of OP to the control of nonlinear physical systems in real-time. We therefore introduce an extension of OP to real-time control, which(More)
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