Quantile-Based Policy Optimization for Reinforcement Learning. (arXiv:2201.11463v1 [cs.LG])

Web: http://arxiv.org/abs/2201.11463

Classical reinforcement learning (RL) aims to optimize the expected
cumulative rewards. In this work, we consider the RL setting where the goal is
to optimize the quantile of the cumulative rewards. We parameterize the policy
controlling actions by neural networks and propose a novel policy gradient
algorithm called Quantile-Based Policy Optimization (QPO) and its variant
Quantile-Based Proximal Policy Optimization (QPPO) to solve deep RL problems
with quantile objectives. QPO uses two coupled iterations running at different
time scales for simultaneously …

arxiv learning optimization policy reinforcement learning