منابع مشابه
Risk-Sensitive Reinforcement Learning
We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of the underlying Markov decision process. When a...
متن کاملRisk-Sensitive Approaches for Reinforcement Learning
When designing a control for a dynamical system, the notion of risk is often related to constraints occurring for parameters of the state space. These constraints denote forbidden or error states of the system, e.g. obstacles in robot control or dangerous states in process engineering. In the first part of this book, I will review potential and Lyapunov-based methods for incorporating knowledge...
متن کاملRisk-sensitive Inverse Reinforcement Learning via Coherent Risk Models
The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive IRL in order to explicitly account for an expert’...
متن کاملCost-Sensitive Reinforcement Learning
We introduce cost-sensitive regression as a way to introduce information obtained by planning as background knowledge into a relational reinforcement learning algorithm. By offering a trade-off between using knowledge rich, but computationally expensive knowledge resulting from planning like approaches such as minimax search and computationally cheap, but possibly incorrect generalizations, the...
متن کاملRisk-Sensitive Inverse Reinforcement Learning via Gradient Methods
We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. We derive a risk-sensitive reinforcement learning algorithm with convergence guarantees that employs convex risk metrics and models of human decisionmaking deriving from behavioral economics. The risk-sensitive reinforcement learning algorithm provides the theoretical underpi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Computation
سال: 2014
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco_a_00600