← Back to papers
2011 NIPS NeurIPS 2011

Transfer from Multiple MDPs

Abstract

Transfer reinforcement learning (RL) methods leverage on the experience collected on a set of source tasks to speed-up RL algorithms. A simple and effective approach is to transfer samples from source tasks and include them in the training set used to solve a target task. In this paper, we investigate the theoretical properties of this transfer method and we introduce novel algorithms adapting the transfer process on the basis of the similarity between source and target tasks. Finally, we report illustrative experimental results in a continuous chain problem.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Reinforcement Learning
📈 Trend Setter — Transfer Learning
🧭 Keyword Pioneer — transfer reinforcement learning
🐣 Hot Topic Early Bird — reinforcement learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics

Authors

Alessandro Lazaric , Marcello Restelli

Topics

Artificial Intelligence > Core AI > Agent Systems Artificial Intelligence > Learning Paradigms > Transfer Learning Reinforcement Learning > Methods > Deep RL Machine Learning > Learning Types > Reinforcement Learning Machine Learning > Learning Types > Transfer Learning

Keywords

reinforcement learning transfer learning markov decision process mdp transfer reinforcement learning target task sample transfer source task
Download PDF

Related papers

Co-Training for Domain Adaptation 2011
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning 2011
Learning to Agglomerate Superpixel Hierarchies 2011
A Reinforcement Learning Theory for Homeostatic Regulation 2011
A Global Structural EM Algorithm for a Model of Cancer Progression 2011