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Towards Inverse Reinforcement Learning for Limit Order Book Dynamics

We investigate whether Inverse Reinforcement Learning (IRL) can infer rewards from agents within real financial stochastic environments: limit order books (LOB). Our results illustrate that complex behaviours, induced by non-linear reward functions amid agent-based stochastic scenarios, can be deduced through inference, encouraging the use of inverse reinforcement learning for opponent-modelling in multi-agent systems.

Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva
Oral Presentation, Multi-Agent Learning Workshop at the 36th International Conference on Machine Learning, 2019
[arXiv] [BibTex]

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