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 Pitfall!
Distributional Reinforcement Learning with Quantile Regression
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.
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
• 
Efficient exploration remains a major challenge for reinforcement learning.
ATARI GAMES DISTRIBUTIONAL REINFORCEMENT LEARNING EFFICIENT EXPLORATION Q-LEARNING
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.
Estimating Risk and Uncertainty in Deep Reinforcement Learning
• uncharted-technologies/risk-and-uncertainty
• 
Our method combines elements from distributional reinforcement learning and approximate Bayesian inference techniques with neural networks, allowing us to disentangle both types of uncertainty on the expected return of a policy.
BAYESIAN INFERENCE DISTRIBUTIONAL REINFORCEMENT LEARNING EFFICIENT EXPLORATION