Now showing items 1-3 of 3
Learning dynamic processes over graphs
(Georgia Institute of Technology, 2020-07-09)
Graphs appear as a versatile representation of information across domains spanning social networks, biological networks, transportation networks, molecular structures, knowledge networks, web information network and many ...
Learning Nash equilibria in zero-sum stochastic games via entropy-regularized policy approximation
(Georgia Institute of Technology, 2020-07-27)
In this thesis, we explore the use of policy approximation for reducing the computational cost of learning Nash Equilibria in Multi-Agent Reinforcement Learning. Existing multi-agent reinforcement learning methods are ...
Value methods for efficiently solving stochastic games of complete and incomplete information
(Georgia Institute of Technology, 2013-08-23)
Multi-agent reinforcement learning (MARL) poses the same planning problem as traditional reinforcement learning (RL): What actions over time should an agent take in order to maximize its rewards? MARL tackles a challenging ...