  Sharing knowledge between tasks is vital for efficient learning in a
multi-task setting. However, most research so far has focused on the easier
case where knowledge transfer is not harmful, i.e., where knowledge from one
task cannot negatively impact the performance on another task. In contrast, we
present an approach to multi-task deep reinforcement learning based on
attention that does not require any a-priori assumptions about the
relationships between tasks. Our attention network automatically groups task
knowledge into sub-networks on a state level granularity. It thereby achieves
positive knowledge transfer if possible, and avoids negative transfer in cases
where tasks interfere. We test our algorithm against two state-of-the-art
multi-task/transfer learning approaches and show comparable or superior
performance while requiring fewer network parameters.
