Reinforcement learning methods have successfully been applied to build autonomous agents that solve many sequential decision making problems. However, agents need a long time to learn a suitable policy, specially when multiple autonomous agents are in the environment. This research aims to propose a Transfer Learning (TL) framework to accelerate learning by exploiting two knowledge sources: (i) previously learned tasks; and (ii) advising from a more experienced agent. The definition of such framework requires answering several challenging research questions, including: How to abstract and represent knowledge, in order to allow generalization and posterior reuse?, How and when to transfer and receive knowledge in an efficient manner?, and How to evaluate the transfer quality in a Multiagent scenario?.
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