Local Optimization for Simulation of Natural Motion

Reinforcement Learning (Sutton and Barto 1998) is a theoretical framework for optimizing the behavior of artificial agents. The notion that behavior in the natural world is in some sense optimal is explored by areas such as biomechanics and physical anthropology. These fields propose a variety of candidate optimality criteria (?, e.g.,)]eng,cost as possible formulations of the principles underlying natural motion. Recent developments in computational biomechanics allow us to create articulated models of living creatures with a significant degree of biological realism (Delp et al. 2007). I aim to bring these elements together in my research by using Reinforcement Learning to generate optimized behavior in biomechanical simulations. Such a generative approach will allow us to examine critically postulated optimality criteria and investigate hypotheses that cannot be easily studied in the real world.