Learning to plan in large domains with deep neural networks

Abstract

In the domain of artificial intelligence, effective and efficient planning is one key factor to developing an adaptive agent which can solve tasks in complex environments. However, traditional planning algorithms only work properly in small domains. Learning to plan, which requires an agent to apply the knowledge learned from past experience to planning, can scale planning to large domains. Recent advances in deep learning widen the access to better learning techniques. Combining traditional planning algorithms with modern learning techniques in a proper way enables an agent to extract useful knowledge and thus show good performance in large domains. This thesis aims to explore learning to plan in large domains with deep neural networks. The main contributions of this thesis include: (1) a literature survey on learning to plan; (2) proposing a new network architecture that learns from planning, combining this network with a planner, implementing and testing this idea in the game Othello.