Agents
Provable Partially Observable Reinforcement Learning with Privileged Information Yang Cai
Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL).
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback Y unchang Y ang 1 Han Zhong 1 Tianhao Wu2 Bin Liu 3
More examples include but are not limited to robotics (Mahmood et al.,
Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows
In many machine learning tasks, there are commonly sources of exogenous and endogenous distribution shift, necessitating that the algorithm be retrained repeatedly over time.
SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization
This paper studies decentralized bilevel optimization, in which multiple agents collaborate to solve problems involving nested optimization structures with neighborhood communications.
Transportability for Bandits with Data from Different Environments
A unifying theme in the design of intelligent agents is to efficiently optimize a policy based on what prior knowledge of the problem is available and what actions can be taken to learn more about it.
