A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks

Reward engineering is an important aspect of reinforcement learning. Whether or not the user's intentions can be correctly encapsulated in the reward function can significantly impact the learning outcome. Current methods rely on manually crafted reward functions that often require parameter tuning to obtain the desired behavior. This operation can be expensive when exploration requires systems to interact with the physical world. In this paper, we explore the use of temporal logic (TL) to specify tasks in reinforcement learning. TL formula can be translated to a real-valued function that measures its level of satisfaction against a trajectory. We take advantage of this function and propose temporal logic policy search (TLPS), a model-free learning technique that finds a policy that satisfies the TL specification. A set of simulated experiments are conducted to evaluate the proposed approach

The Gender Wage Gap and Pay Equity: Is Comparable Worth the Next Step?

This report examines the trend in the male-female wage gap and the explanations offered for its existence. Remedies proposed for the gender wage gap’s amelioration are addressed, with an in-depth focus on the comparable worth approach to achieving “pay equity” or “fair pay” between women and men

Deconstructing a Discipline. A Review of Taking Back Philosophy: A Multicultural Manifesto by Bryan Van Norden (Columbia University Press, 2017)

A review of Taking Back Philosophy: A Multicultural Manifesto by Bryan Van Norden (Columbia University Press, 2017).Page numbers for this review were updated on 01/05/2021