  We propose a novel framework for multitask reinforcement learning based on
the minimum description length (MDL) principle. In this approach, which we term
MDL-control (MDL-C), the agent learns the common structure among the tasks with
which it is faced and then distills it into a simpler representation which
facilitates faster convergence and generalization to new tasks. In doing so,
MDL-C naturally balances adaptation to each task with epistemic uncertainty
about the task distribution. We motivate MDL-C via formal connections between
the MDL principle and Bayesian inference, derive theoretical performance
guarantees, and demonstrate MDL-C's empirical effectiveness on both discrete
and high-dimensional continuous control tasks.
