Reinforcement learning (RL) is one of the foundational pillars of
artificial intelligence and machine learning. An important
consideration in any optimization or control problem is the notion
of risk, but its incorporation into RL has been a fairly recent
development. This monograph surveys research on risk-sensitive RL
that uses policy gradient search. The authors survey some of the
recent work in this area specifically where policy gradient search
is the solution approach. In the first risk-sensitive RL setting,
they cover popular risk measures based on variance, conditional
value at-risk and chance constraints, and present a template for
policy gradient-based risk-sensitive RL algorithms using a
Lagrangian formulation. For the setting where risk is incorporated
directly into the objective function, they consider an exponential
utility formulation, cumulative prospect theory, and coherent risk
measures. Written for novices and experts alike the authors have
made the text completely self-contained but also organized in a
manner that allows expert readers to skip background chapters. This
is a complete guide for students and researchers working on this
aspect of machine learning.
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