Temporal abstraction and generalisation in reinforcement learning

Thesis

The ability of agents to generalise---to perform well when presented with previously unseen situations and data---is deeply important to the reliability, autonomy, and functionality of artificial intelligence systems. The generalisation test examines an agent's ability to reason over the world in an \emph{abstract} manner. In reinforcement learning problem settings, where an agent interacts continually with the environment, multiple notions of abstraction are possible. State-based abstraction...

Expand abstract

Authors: Smith, M

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• DOI: 10.5287/ora-ovrpagmb0
• Language: English
• Keywords: imitation learning; generalisation; reinforcement learning; target networks; temporal abstraction
• Subjects: Reinforcement learning