Search Results for author:
Found 9 papers, 2 papers with code
Simulate and Optimise: A two-layer mortgage simulator for designing novel mortgage assistance products
We develop a novel two-layer approach for optimising mortgage relief products through a simulated multi-agent mortgage environment.
Learning Robust Reward Machines from Noisy Labels
This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces.
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning
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).
Learning Reward Machines in Cooperative Multi-Agent Tasks
This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) that combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks.
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning
Knowing this information can help reduce the sample complexity of RL algorithms by masking the inapplicable actions from the policy distribution to only explore actions relevant to finding an optimal policy.
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
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.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Phantom -- A RL-driven multi-agent framework to model complex systems
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 and allowing the system dynamics to emerge from their interactions.
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP
In this paper, we evaluate the use of Reinforcement Learning (RL) to solve a classic combinatorial optimization problem: the Capacitated Vehicle Routing Problem (CVRP).
Towards a fully RL-based Market Simulator
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.
