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This paper introduces the Intentional Unintentional (IU) agent. This agentendows the deep deterministic policy gradients (DDPG) agent for continuouscontrol with the ability to solve several tasks simultaneously. Learning tosolve many tasks simultaneously has been a long-standing, core goal ofartificial intelligence, inspired by infant development and motivated by thedesire to build flexible robot manipulators capable of many diverse behaviours.We show that the IU agent not only learns to solve many tasks simultaneouslybut it also learns faster than agents that target a single task at-a-time. Insome cases, where the single task DDPG method completely fails, the IU agentsuccessfully solves the task. To demonstrate this, we build a playroomenvironment using the MuJoCo physics engine, and introduce a grounded formallanguage to automatically generate tasks.

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously