  Inverse reinforcement learning has proved its ability to explain state-action
trajectories of expert agents by recovering their underlying reward functions
in increasingly challenging environments. Recent advances in adversarial
learning have allowed extending inverse RL to applications with non-stationary
environment dynamics unknown to the agents, arbitrary structures of reward
functions and improved handling of the ambiguities inherent to the ill-posed
nature of inverse RL. This is particularly relevant in real time applications
on stochastic environments involving risk, like volatile financial markets.
Moreover, recent work on simulation of complex environments enable learning
algorithms to engage with real market data through simulations of its latent
space representations, avoiding a costly exploration of the original
environment. In this paper, we explore whether adversarial inverse RL
algorithms can be adapted and trained within such latent space simulations from
real market data, while maintaining their ability to recover agent rewards
robust to variations in the underlying dynamics, and transfer them to new
regimes of the original environment.
