Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Proto-value functions: developmental reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Transfer via inter-task mappings in policy search reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Learning Representation and Control in Markov Decision Processes: New Frontiers
Foundations and Trends® in Machine Learning
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The aim of transfer learning is to accelerate learning in related domains. In reinforcement learning, many different features such as a value function and a policy can be transferred from a source domain to a related target domain. Many researches focused on transfer using hand-coded translation functions that are designed by the experts a priori. However, it is not only very costly but also problem dependent. We propose to apply the Graph Laplacian that is based on the spectral graph theory to decompose the value functions of both a source domain and a target domain into a sum of the basis functions respectively. The transfer learning can be carried out by transferring weights on the basis functions of a source domain to a target domain. We investigate two types of domain transfer, scaling and topological. The results demonstrated that the transferred policy is a better prior policy to reduce the learning time.