Contingency-Aware Exploration in Reinforcement Learning

Understand

This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning.

• To investigate this question, we consider an instantiation of this hypothesis evaluated on the Arcade Learning Element (ALE).
• In this study, we develop an attentive dynamics model (ADM) that discovers controllable elements of the observations, which are often associated with the location of the character in Atari games.
• The ADM is trained in a self-supervised fashion to predict the actions taken by the agent.