  In stochastic games with incomplete information, the uncertainty is evoked by
the lack of knowledge about a player's own and the other players' types, i.e.
the utility function and the policy space, and also the inherent stochasticity
of different players' interactions. In existing literature, the risk in
stochastic games has been studied in terms of the inherent uncertainty evoked
by the variability of transitions and actions. In this work, we instead focus
on the risk associated with the \textit{uncertainty over types}. We contrast
this with the multi-agent reinforcement learning framework where the other
agents have fixed stationary policies and investigate risk-sensitiveness due to
the uncertainty about the other agents' adaptive policies. We propose
risk-sensitive versions of existing algorithms proposed for risk-neutral
stochastic games, such as Iterated Best Response (IBR), Fictitious Play (FP)
and a general multi-objective gradient approach using dual ascent (DAPG). Our
experimental analysis shows that risk-sensitive DAPG performs better than
competing algorithms for both social welfare and general-sum stochastic games.

Machine Learning;Multiagent Systems