  We study the benefit of sharing representations among tasks to enable the
effective use of deep neural networks in Multi-Task Reinforcement Learning. We
leverage the assumption that learning from different tasks, sharing common
properties, is helpful to generalize the knowledge of them resulting in a more
effective feature extraction compared to learning a single task. Intuitively,
the resulting set of features offers performance benefits when used by
Reinforcement Learning algorithms. We prove this by providing theoretical
guarantees that highlight the conditions for which is convenient to share
representations among tasks, extending the well-known finite-time bounds of
Approximate Value-Iteration to the multi-task setting. In addition, we
complement our analysis by proposing multi-task extensions of three
Reinforcement Learning algorithms that we empirically evaluate on widely used
Reinforcement Learning benchmarks showing significant improvements over the
single-task counterparts in terms of sample efficiency and performance.
