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 ...
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 ...
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 ...