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Found 6 papers, 4 papers with code
Pareto Conditioned Networks
In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process.
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives.
Scalable Optimization for Wind Farm Control using Coordination Graphs
This is a non-trivial optimization problem, as complex dependencies exist between the wind turbines.
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes.
Model-based Reinforcement Learning
Multi-agent Reinforcement Learning
+3
Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures
In this task, wind turbines must coordinate their alignments with respect to the incoming wind vector in order to optimize power production.
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
Learning to coordinate between multiple agents is an important problem in many reinforcement learning problems.
