Reinforcement Learning

Task-agnostic visual exploration policies may be trained through a proxy “observation completion” task that requires an agent to “paint” unobserved views given a small set of observed views.

Active visual perception with realistic and complex imagery can be formulated as an end-to-end reinforcement learning problem, the solution to which benefits from additionally exploiting the auxiliary task of action-conditioned future prediction.