Friday, 29th March, 2019, 10.00 - 11.00

Continual Reinforcement Learning in 3D Non-stationary Environments

Dynamic and always-changing environments constitute an hard challenge for current reinforcement learning techniques. Artificial agents, nowadays, are often trained in very static and reproducible conditions in simulation, where the common assumption is that observations can be sampled i.i.d from the environment. However, tackling more complex problems and real-world settings this can be rarely considered the case, with environments often non-stationary and subject to unpredictable, frequent changes. In this talk we discuss about a new open benchmark for learning continually through reinforce in a complex 3D non-stationary object picking task based on VizDoom and subject to several environmental changes. We further propose a number of end-to-end, model-free continual reinforcement learning strategies showing competitive results even without any access to previously encountered environmental conditions or observations.

Speaker: Vincenzo Lomonaco, DISI Department, University of Bologna, Italy

Room Ramón y Cajal, PRBB Innner square