Adversarial recovery of agent rewards from latent spaces of the limit order book
This work addresses the problem of robust reward recovery in volatile financial markets for researchers and practitioners, but appears incremental as it builds on existing adversarial and latent space methods.
The paper investigates whether adversarial inverse reinforcement learning can be adapted to latent space simulations of real market data to recover agent rewards robust to dynamic variations and transfer them to new regimes, but does not report specific numerical results.
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.