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MetaCURL: Non-stationary Concave Utility Reinforcement Learning
Under partial information on the probability transitions (uncertainty and non-stationarity coming only from external noise, independent of agent state-action pairs), we achieve optimal dynamic regret without prior knowledge of MDP changes.
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent
Many machine learning tasks can be solved by minimizing a convex function of an occupancy measure over the policies that generate them.
Reimagining Demand-Side Management with Mean Field Learning
Integrating renewable energy into the power grid while balancing supply and demand is a complex issue, given its intermittent nature.
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