Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Motivated reinforcement learning for adaptive characters in open-ended simulation games
Proceedings of the international conference on Advances in computer entertainment technology
Evolution Strategies for Direct Policy Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Reinforcement learning with perceptual aliasing: the perceptual distinctions approach
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
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In reinforcement learning of long-term tasks, learning efficiency may deteriorate when an agent's probabilistic actions cause too many mistakes before task learning reaches its goal. The new type of state we propose --- fixed mode --- to which a normal state shifts if it has already received sufficient reward --- chooses an action based on a greedy strategy, eliminating randomness of action selection and increasing efficiency. We start by proposing the combining of an algorithm with penalty avoiding rational policy making and online profit sharing with fixed mode states. We then discuss the target system and learning-controller design. In simulation, the learning task involves stabilizing of biped walking by using the learning controller to modify a robot's waist trajectory. We then discuss simulation results and the effectiveness of our proposal.