Reinforcement Learning
7b647a7d88f4d6319bf0d600d168dbeb-Supplemental.pdf
It is therefore difficult to discuss and understand what choices, among the high-levelalgorithmic options as well as low-levelimplementation details, matter.
7b647a7d88f4d6319bf0d600d168dbeb-Paper.pdf
It is therefore difficult to discuss and understand what choices, among the high-levelalgorithmic options as well as low-levelimplementation details, matter.
992f0fed0720dbb9d4e060d03ed531ba-Supplemental.pdf
Decision making in these systems has a long history, yet, if the state is not fully observed acting optimally in such systems is notoriously hard.
PAC: AssistedValueFactorisationwithCounterfactual PredictionsinMulti-AgentReinforcementLearning
To enable decentralized execution, we alsoderivefactorized per-agentpolicies inspired byamaximum-entropyMARL framework.
Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization
Stochastic approximation is a foundation for many algorithms found in machine learning and optimization.
792c7b5aae4a79e78aaeda80516ae2ac-Paper.pdf
Such problems arise, for example, in reinforcement learning, engineering design, and manufacturing.
