Evaluating Learned State Representations for Atari

Deep reinforcement learning, the combination of deep learning and reinforcement learning, has enabled the training of agents that can solve complex tasks from visual inputs. However, these methods often require prohibitive amounts of computation to obtain successful results. To improve learning efficiency, there has been a renewed focus on separating state representation and policy learning. In this paper, we investigate the quality of state representations learned by different types of autoencoders, a popular class of neural networks used for representation learning. We assess not only the quality of the representations learned by undercomplete, variational, and disentangled variational autoencoders, but also how the quality of the learned representations is affected by changes in representation size. To accomplish this, we also present a new method for evaluating learned state representations for Atari games using the Atari Annotated RAM Interface. Our findings highlight differences in the quality of state representations learned by different types of autoencoders and their robustness to reduction in representation size. Our results also demonstrate the advantage of using more sophisticated evaluation methods over assessing reconstruction quality.