A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games

Understand

Reinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown.

• State-of-the-art reinforcement learning approaches, such as deep Q-networks, are model-free and learn to act effectively across a wide range of environments such as Atari games, but require huge amounts of data.
• Model-based techniques are more data-efficient, but need to acquire explicit knowledge about the environment.
• In this paper, we take a step towards using model-based techniques in environments with a high-dimensional visual state space by demonstrating that it is possible to learn system dynamics and the reward structure jointly.