Reinforcement Learning
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.
Uniform-PACBoundsforReinforcementLearning withLinearFunctionApproximation
Designing efficient reinforcement learning (RL) algorithms for environments with large state and action spaces is one of the main tasks in the RL community.