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Found 7 papers, 1 papers with code
TacticAI: an AI assistant for football tactics
The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC.
BRExIt: On Opponent Modelling in Expert Iteration
Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies).
Online Planning in POMDPs with Self-Improving Simulators
To plan reliably and efficiently while the approximate simulator is learning, we develop a method that adaptively decides which simulator to use for every simulation, based on a statistic that measures the accuracy of the approximate simulator.
Automated Peer-to-peer Negotiation for Energy Contract Settlements in Residential Cooperatives
This paper presents an automated peer-to-peer negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative considering heterogeneity prosumer preferences.
Robust Temporal Difference Learning for Critical Domains
We prove convergence of the operator to the optimal robust Q-function with respect to the model using the theory of Generalized Markov Decision Processes.
Energy Contract Settlements through Automated Negotiation in Residential Cooperatives
This paper presents an automated peer-to-peer (P2P) negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative (REC) considering heterogeneous prosumer preferences.
Multiagent Systems
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves.
