  Classical reinforcement learning (RL) aims to optimize the expected
cumulative reward. In this work, we consider the RL setting where the goal is
to optimize the quantile of the cumulative reward. 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) for solving deep RL problems
with quantile objectives. QPO uses two coupled iterations running at different
timescales for simultaneously updating quantiles and policy parameters, whereas
QPPO is an off-policy version of QPO that allows multiple updates of parameters
during one simulation episode, leading to improved algorithm efficiency. Our
numerical results indicate that the proposed algorithms outperform the existing
baseline algorithms under the quantile criterion.
