Distributional Reinforcement Learning
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Implicit Quantile Networks for Distributional Reinforcement Learning
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
QUOTA: The Quantile Option Architecture for Reinforcement Learning
In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL).
Information-Directed Exploration for Deep Reinforcement Learning
ICLR 2019
• nikonikolov/rltf
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Efficient exploration remains a major challenge for reinforcement learning.
ATARI GAMES DISTRIBUTIONAL REINFORCEMENT LEARNING EFFICIENT EXPLORATION Q-LEARNING
Distributional Reinforcement Learning with Quantile Regression
• ars-ashuha/quantile-regression-dqn-pytorch
• 
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.
GAN Q-learning
Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation.