The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.
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In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN.
SOTA for Atari Games on Atari 2600 Beam Rider
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.
SOTA for Atari Games on Atari 2600 Asteroids
In recent years there have been many successes of using deep representations in reinforcement learning.
SOTA for Atari Games on Atari 2600 Bank Heist
Extending the idea of a locally consistent operator, we then derive sufficient conditions for an operator to preserve optimality, leading to a family of operators which includes our consistent Bellman operator.
Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.
In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean.