Program Guided Agent

Shao-Hua Sun, Te-Lin Wu, Joseph J. Lim

Keywords: generalization, learning to execute, nlp

Wed Session 1 (05:00-07:00 GMT) [Live QA] [Cal]
Wed Session 2 (08:00-10:00 GMT) [Live QA] [Cal]
Wednesday: RL and Planning

Abstract: Developing agents that can learn to follow natural language instructions has been an emerging research direction. While being accessible and flexible, natural language instructions can sometimes be ambiguous even to humans. To address this, we propose to utilize programs, structured in a formal language, as a precise and expressive way to specify tasks. We then devise a modular framework that learns to perform a task specified by a program – as different circumstances give rise to diverse ways to accomplish the task, our framework can perceive which circumstance it is currently under, and instruct a multitask policy accordingly to fulfill each subtask of the overall task. Experimental results on a 2D Minecraft environment not only demonstrate that the proposed framework learns to reliably accomplish program instructions and achieves zero-shot generalization to more complex instructions but also verify the efficiency of the proposed modulation mechanism for learning the multitask policy. We also conduct an analysis comparing various models which learn from programs and natural language instructions in an end-to-end fashion.

Similar Papers

Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn,
Guiding Program Synthesis by Learning to Generate Examples
Larissa Laich, Pavol Bielik, Martin Vechev,
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, Ming-Hsuan Yang,
Compositional Language Continual Learning
Yuanpeng Li, Liang Zhao, Kenneth Church, Mohamed Elhoseiny,