Since 1907, one Rockport family have continued to make their timeless soda pop the old-fashioned way. Twin Lights Soda--or tonic, as it's still known locally--was started by second-generation Portuguese immigrants in the back of a small-town family grocer and named after the iconic pair of lighthouses just off the coast of Cape Ann. The bottling industry was one of America's great entrepreneurial endeavors, and at its peak, Twin Lights outsold even the two largest national cola brands in the region. But today, while soft drinks are a $45 billion industry, few independents remain. Authors Paul St. Germain and Dev Sherlock trace the fascinating story of one of the last family bottlers still in operation.
Afterlives of Romantic Intermediality addresses the manifold, even global artistic developments that were initiated by European Romantics. In the first section, the contributors show how the rising perspective of intermediality was discussed in philosophical terms and adapted itself to Romantic literature and music. In the second section, the contributors show how post-Romantic writers, visual artists, and composers have engaged with Romantic heritage. By exploring primary works that range from European arts to Latin American literature, these essays focus on the interdisciplinary developments that have emerged in literature, music, painting, film, architecture, and video art. Overall, the contributions in this volume demonstrate that intermedial connections—or sometimes the conscious lack of such connections—embody intriguing aspects of modernity and postmodernity.
The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment. The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions.
