Unsupervised State Representation Learning in Atari
It addresses the problem of learning latent generative factors without supervision for building intelligent agents, but it is incremental as it builds on existing representation learning approaches.
The paper tackles unsupervised state representation learning in Atari games by introducing a method that maximizes mutual information across features, resulting in a new benchmark for evaluation and competitive performance compared to other state-of-the-art methods.
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state variables. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods. The code associated with this work is available at https://github.com/mila-iqia/atari-representation-learning