DeepMind, London, UK; University of Alberta, Edmonton, Canada; Omron Sinic X, Tokyo, Japan; DeepMind, London, UK; University of Alberta, Edmonton, Canada; DeepMind, London, UK; University College London, London, UK
Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvari
Approximate Policy Iteration;Reinforcement Learning;Planning;Markov Decision Processes;Linear Function Approximation;Sample Complexity
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs