Nacionalni portal odprte znanosti

This paper extends many of the recent popular reinforcement learning (RL) algorithms to a generalized framework that includes least-squares temporal difference (LSTD) learning, least-squares policy evaluation (LSPE) and a variant of incremental LSTD (iLSTD). The basis of this extension is a precondi ...

Dyna planning is an efficient way of learning from real and imaginary experience. Existing tabular and linear Dyna algorithms are single-step, because an "imaginary" feature is predicted only one step into the future. In this paper, we introduce a multi-step Dyna planning that predicts more st ...