  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
into the training set used to solve a given 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.
