Sketch-Based Linear Value Function Approximation

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Playing Atari with Deep Reinforcement Learning (2013, neutral): “Subsequently, results were improved by using a larger number of features, and using tug-of-war hashing to randomly project the features into a lower-dimensional space [2].”
A Neuroevolution Approach to General Atari Game Playing (2014, methodology): “ALE has been subsequently used as a domain for reinforcement learning and search agents [2], [3] and to investigate aspects of prediction and self-detection [4].”
Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games (2016, background): “There are a number of approaches to building ATARI game players, including some that do not use DL (e.g., Bellemare et al. 2012a, 2012b, 2013a, Lipovetzky et al. 2015).”
State of the Art Control of Atari Games Using Shallow Reinforcement Learning (2015, methodology): “These feature sets were, for some time, the standard representations for Atari 2600 games, being directly used in other work [7, 16, 17] or serving as the base for more elaborate feature sets [4].”
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning (2014, background): “Specifically, low-level features are learned to encode low-level statistical dependencies (e.g., “edges” in images), and higher-level features encode higher-order dependencies of the lower-level features (e.g., “object parts”) [14].”