  When multiple agents learn in a decentralized manner, the environment appears
non-stationary from the perspective of an individual agent due to the
exploration and learning of the other agents. Recently proposed deep
multi-agent reinforcement learning methods have tried to mitigate this
non-stationarity by attempting to determine which samples are from other agent
exploration or suboptimality and take them less into account during learning.
Based on the same philosophy, this paper introduces a decentralized quantile
estimator, which aims to improve performance by distinguishing non-stationary
samples based on the likelihood of returns. In particular, each agent considers
the likelihood that other agent exploration and policy changes are occurring,
essentially utilizing the agent's own estimations to weigh the learning rate
that should be applied towards the given samples. We introduce a formal method
of calculating differences of our return distribution representations and
methods for utilizing it to guide updates. We also explore the effect of
risk-seeking strategies for adjusting learning over time and propose adaptive
risk distortion functions which guides risk sensitivity. Our experiments, on
traditional benchmarks and new domains, show our methods are more stable,
sample efficient and more likely to converge to a joint optimal policy than
previous methods.
