Omega-Regular Objectives in Model-Free Reinforcement Learning

We provide the first solution for model-free reinforcement learning of ω-regular objectives for Markov decision processes (MDPs). We present a constructive reduction from the almost-sure satisfaction of ω-regular bjectives to an almost-sure reachability problem, and extend this technique to learning how to control an unknown model so that the chance of satisfying the objective is maximized. We compile ω-regular properties into limit-deterministic B¨uchi automata instead of the traditional Rabin automata; this choice sidesteps difficulties that have marred previous proposals. Our approach allows us to apply model-free, off-the-shelf reinforcement learning algorithms to compute optimal strategies from the observations of the MDP. We present an experimental evaluation of our technique on benchmark learning problems

'Dressed in a Little Brief Authority': Authority Before, During, and After Shakespeare's Plays

This essay discusses the concept of authority from Shakespeare’s time to our own, considers examples of Shakespeare’s own explorations of authority, and provides a concise history of the ways in which Shakespeare has been constructed as cultural authority in different historical periods. It summarises the essays contained in the book, and argues that the book illuminates not only how Shakespeare became the archetypal figure of English cultural authority, but, perhaps more interestingly, why