Zero-Shot Task Generalization with Multi-Task Deep Reinforcement\n Learning

Abstract

As a step towards developing zero-shot task generalization capabilities in\nreinforcement learning (RL), we introduce a new RL problem where the agent\nshould learn to execute sequences of instructions after learning useful skills\nthat solve subtasks. In this problem, we consider two types of generalizations:\nto previously unseen instructions and to longer sequences of instructions. For\ngeneralization over unseen instructions, we propose a new objective which\nencourages learning correspondences between similar subtasks by making\nanalogies. For generalization over sequential instructions, we present a\nhierarchical architecture where a meta controller learns to use the acquired\nskills for executing the instructions. To deal with delayed reward, we propose\na new neural architecture in the meta controller that learns when to update the\nsubtask, which makes learning more efficient. Experimental results on a\nstochastic 3D domain show that the proposed ideas are crucial for\ngeneralization to longer instructions as well as unseen instructions.\n