Collect and Infer

Data-efficiency is a core requirement for artificial intelligence. We are investigating agent architectures and methods, that implement reinforcement learning from scratch based on the principle of 'Collect and Infer' and investigate their application to challenging control and robotic domains. 'Collect and Infer' focuses on the two important aspects of agent learning:

1. Collecting the 'right data' by advanced exploration methods, like 'Learning by playing' (Riedmiller et.al, ICML 2018)

2. Effective inference of knowledge from a database of collected transition data, like the 'MPO' algorithm (Maximum A Posteriori Policy Optimisation, Abdolmaleki et.al, 2018) or 'ABM', a highly effective batch RL method (Siegel et. al, ICML 2020)