  Trading markets represent a real-world financial application to deploy
reinforcement learning agents, however, they carry hard fundamental challenges
such as high variance and costly exploration. Moreover, markets are inherently
a multiagent domain composed of many actors taking actions and changing the
environment. To tackle these type of scenarios agents need to exhibit certain
characteristics such as risk-awareness, robustness to perturbations and low
learning variance. We take those as building blocks and propose a family of
four algorithms. First, we contribute with two algorithms that use risk-averse
objective functions and variance reduction techniques. Then, we augment the
framework to multi-agent learning and assume an adversary which can take over
and perturb the learning process. Our third and fourth algorithms perform well
under this setting and balance theoretical guarantees with practical use.
Additionally, we consider the multi-agent nature of the environment and our
work is the first one extending empirical game theory analysis for multi-agent
learning by considering risk-sensitive payoffs.
