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Distributional Reinforcement Learning with Quantile Regression
[article]
2017
arXiv
pre-print
In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions, rewards, and actions can all induce randomness in the observed long-term return. Traditionally, reinforcement learning algorithms average over this randomness to estimate the value function. In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the
arXiv:1710.10044v1
fatcat:46adgv3uwjbmnkoe6ccavradyu