16 citations · 32 across the 19 of their papers we have counts for
5 papers · 1 filter
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning
Leo Ardon, Alberto Pozanco, Daniel Borrajo +1
Reinforcement Learning (RL) algorithms are known to scale poorly to environments with many available actions, requiring numerous samples to learn an optimal policy. The traditional…
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…
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP
Leo Ardon
In this paper, we evaluate the use of Reinforcement Learning (RL) to solve a classic combinatorial optimization problem: the Capacitated Vehicle Routing Problem (CVRP). We formaliz…
Towards a Fairer Digital Marketing Model
Leo Ardon, Dario Morelli, Francesco Villani +1
Surfing on the internet boom, the digital marketing industry has seen an exponential growth in the recent years and is often at the origin of the financial success of the biggest t…