  This paper introduces the Intentional Unintentional (IU) agent. This agent
endows the deep deterministic policy gradients (DDPG) agent for continuous
control with the ability to solve several tasks simultaneously. Learning to
solve many tasks simultaneously has been a long-standing, core goal of
artificial intelligence, inspired by infant development and motivated by the
desire to build flexible robot manipulators capable of many diverse behaviours.
We show that the IU agent not only learns to solve many tasks simultaneously
but it also learns faster than agents that target a single task at-a-time. In
some cases, where the single task DDPG method completely fails, the IU agent
successfully solves the task. To demonstrate this, we build a playroom
environment using the MuJoCo physics engine, and introduce a grounded formal
language to automatically generate tasks.
