16 citations · 32 across the 19 of their papers we have counts for
4 papers · 1 filter
Simulate and Optimise: A two-layer mortgage simulator for designing novel mortgage assistance products
Leo Ardon, Benjamin Patrick Evans, Deepeka Garg +3
We develop a novel two-layer approach for optimising mortgage relief products through a simulated multi-agent mortgage environment. While the approach is generic, here the environm…
Learning Robust Reward Machines from Noisy Labels
Roko Parac, Lorenzo Nodari, Leo Ardon +3
This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces. The key aspect of RM-driven R…
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning
Giuseppe Canonaco, Leo Ardon, Alberto Pozanco +1
The use of Potential-Based Reward Shaping (PBRS) has shown great promise in the ongoing research effort to tackle sample inefficiency in Reinforcement Learning (RL). However, choos…
A Heterogeneous Agent Model of Mortgage Servicing: An Income-based Relief Analysis
Deepeka Garg, Benjamin Patrick Evans, Leo Ardon +5
Mortgages account for the largest portion of household debt in the United States, totaling around $12 trillion nationwide. In times of financial hardship, alleviating mortgage bur…