16 citations · 33 across the 9 of their papers we have counts for
11 papers
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
Nelson Vadori, Leo Ardon, Sumitra Ganesh +7
We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange. We show how a su…
Phantom -- A RL-driven multi-agent framework to model complex systems
Leo Ardon, Jared Vann, Deepeka Garg +2
Agent based modelling (ABM) is a computational approach to modelling complex systems by specifying the behaviour of autonomous decision-making components or agents in the system an…
Reductive MDPs: A Perspective Beyond Temporal Horizons
Thomas Spooner, Rui Silva, Joshua Lockhart +2
Solving general Markov decision processes (MDPs) is a computationally hard problem. Solving finite-horizon MDPs, on the other hand, is highly tractable with well known polynomial-t…
Towards a fully RL-based Market Simulator
Leo Ardon, Nelson Vadori, Thomas Spooner +3
We present a new financial framework where two families of RL-based agents representing the Liquidity Providers and Liquidity Takers learn simultaneously to satisfy their objective…
Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification
Neema Kotonya, Thomas Spooner, Daniele Magazzeni +1
This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. W…
Counterfactual Explanations for Arbitrary Regression Models
Thomas Spooner, Danial Dervovic, Jason Long +3
We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally co…
Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures
Nelson Vadori, Rahul Savani, Thomas Spooner +1
Cheung and Piliouras (2020) recently showed that two variants of the Multiplicative Weights Update method - OMWU and MWU - display opposite convergence properties depending on whet…
Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs
Thomas Spooner, Nelson Vadori, Sumitra Ganesh
Policy gradient methods can solve complex tasks but often fail when the dimensionality of the action-space or objective multiplicity grow very large. This occurs, in part, because…
A Natural Actor-Critic Algorithm with Downside Risk Constraints
Thomas Spooner, Rahul Savani
Existing work on risk-sensitive reinforcement learning - both for symmetric and downside risk measures - has typically used direct Monte-Carlo estimation of policy gradients. While…
Robust Market Making via Adversarial Reinforcement Learning
Thomas Spooner, Rahul Savani
We show that adversarial reinforcement learning (ARL) can be used to produce market marking agents that are robust to adversarial and adaptively-chosen market conditions. To apply…
Market Making via Reinforcement Learning
Thomas Spooner, John Fearnley, Rahul Savani +1
Market making is a fundamental trading problem in which an agent provides liquidity by continually offering to buy and sell a security. The problem is challenging due to inventory…