Proceedings of the AAAI Conference on Artificial Intelligence
Recent advances in efficient planning in deterministic or stochastic high-dimensional domains wit... more Recent advances in efficient planning in deterministic or stochastic high-dimensional domains with continuous action spaces leverage backpropagation through a model of the environment to directly optimize action sequences. However, existing methods typically do not take risk into account when optimizing in stochastic domains, which can be incorporated efficiently in MDPs by optimizing a nonlinear utility function of the return distribution. We bridge this gap by introducing Risk-Aware Planning using PyTorch (RAPTOR), a novel unified framework for risk-sensitive planning through end-to-end optimization of commonly-studied risk-sensitive utility functions such as entropic utility, mean-variance optimization and CVaR. A key technical difficulty of our approach is that direct optimization of general risk-sensitive utility functions by backpropagation is impossible due to the presence of environment stochasticity. The novelty of RAPTOR lies in leveraging reparameterization of the state d...
Using narrative rhetorical analysis, this thesis examines two popular American Indian autobiograp... more Using narrative rhetorical analysis, this thesis examines two popular American Indian autobiographies to analyze the rhetorical impact. By applying Frye's genre theory, I argue that American Indians surpass genre conventions as an act of survivance
Proceedings of the AAAI Conference on Artificial Intelligence
Recent advances in efficient planning in deterministic or stochastic high-dimensional domains wit... more Recent advances in efficient planning in deterministic or stochastic high-dimensional domains with continuous action spaces leverage backpropagation through a model of the environment to directly optimize action sequences. However, existing methods typically do not take risk into account when optimizing in stochastic domains, which can be incorporated efficiently in MDPs by optimizing a nonlinear utility function of the return distribution. We bridge this gap by introducing Risk-Aware Planning using PyTorch (RAPTOR), a novel unified framework for risk-sensitive planning through end-to-end optimization of commonly-studied risk-sensitive utility functions such as entropic utility, mean-variance optimization and CVaR. A key technical difficulty of our approach is that direct optimization of general risk-sensitive utility functions by backpropagation is impossible due to the presence of environment stochasticity. The novelty of RAPTOR lies in leveraging reparameterization of the state d...
Using narrative rhetorical analysis, this thesis examines two popular American Indian autobiograp... more Using narrative rhetorical analysis, this thesis examines two popular American Indian autobiographies to analyze the rhetorical impact. By applying Frye's genre theory, I argue that American Indians surpass genre conventions as an act of survivance
Uploads
Papers by Noah Patton