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Talk
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
Junhyuk Oh · Satinder Singh · Honglak Lee · Pushmeet Kohli

Tue Aug 08 08:48 PM -- 09:06 PM (PDT) @ C4.5
in Deep reinforcement learning 1 »

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

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Author Information

Junhyuk Oh (University of Michigan)
Satinder Singh (University of Michigan)
Honglak Lee (Google / U. Michigan)
Pushmeet Kohli (Microsoft Research)

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