1 citations · 2 across the 6 of their papers we have counts for
6 papers
Explaining an Agent's Future Beliefs through Temporally Decomposing Future Reward Estimators
Mark Towers, Yali Du, Christopher Freeman +1
Future reward estimation is a core component of reinforcement learning agents; i.e., Q-value and state-value functions, predicting an agent's sum of future rewards. Their scalar ou…
The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer
Gregory Everett, Ryan Beal, Tim Matthews +2
In this paper, we present a novel sequential team selection model in soccer. Specifically, we model the stochastic process of player injury and unavailability using player-specific…
TAPE: Leveraging Agent Topology for Cooperative Multi-Agent Policy Gradient
Xingzhou Lou, Junge Zhang, Timothy J. Norman +2
Multi-Agent Policy Gradient (MAPG) has made significant progress in recent years. However, centralized critics in state-of-the-art MAPG methods still face the centralized-decentral…
MADDM: Multi-Advisor Dynamic Binary Decision-Making by Maximizing the Utility
Zhaori Guo, Timothy J. Norman, Enrico H. Gerding
Being able to infer ground truth from the responses of multiple imperfect advisors is a problem of crucial importance in many decision-making applications, such as lending, trading…
PRIME: A Price-Reverting Impact Model of a cryptocurrency Exchange
Christopher J. Cho, Timothy J. Norman, Manuel Nunes
In a financial exchange, market impact is a measure of the price change of an asset following a transaction. This is an important element of market microstructure, which determines…
Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations
Gregory Everett, Ryan J. Beal, Tim Matthews +3
Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS prob…