Markov Decision Processes with a New Optimality Criterion: Discrete Time

By S. C. Jaquette. 1973

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Citations in the corpus

Implicit Quantile Networks for Distributional Reinforcement Learning (2018, background): “Distributional reinforcement learning (Jaquette, 1973; Sobel, 1982; White, 1988; Morimura et al., 2010b; Bellemare et al., 2017) focuses on the intrinsic randomness of returns within the reinforcement learning (RL) framework.”
A Distributional Perspective on Reinforcement Learning (2017, background): “Although the distributional perspective is almost as old as Bellman’s equation itself (Jaquette, 1973; Sobel, 1982; White, 1988), in reinforcement learning it has thus far been subordinated to speciﬁc purposes: to model parametric uncertainty (Dearden et al., 1998), to design risk-sensitive…”
Fully Parameterized Quantile Function for Distributional Reinforcement Learning (2019, background): “Distributional reinforcement learning [Jaquette et al., 1973, Sobel, 1982, White, 1988, Morimura et al., 2010, Bellemare et al., 2017] differs from value-based reinforcement learning in that, instead of focusing only on the expectation of the return, distributional reinforcement learning also takes…”
Risk-averse dynamic programming for Markov decision processes (2010, background): “Models with risk aversion were, so far, represented by separable utility functions; see, among others, [9,12,13,23,24] and the references therein.”
QUOTA: The Quantile Option Architecture for Reinforcement Learning (2018, background): “Thanks to the advances in distributional RL (Jaquette 1973; Bellemare, Dabney, and Munos 2017), we are able to learn the full distribution, not only the mean, for a state-action value.”
An Optimistic Perspective on Offline Reinforcement Learning (2019, background): “A family of recent off-policy deep RL algorithms, which serve as a strong baseline in this paper, include Distributional RL (Bellemare et al., 2017; Jaquette, 1973) methods.”
Distributional Reinforcement Learning for Efficient Exploration (2019, background): “Instead of learning the expected return Q , distributional RL focuses on learning the full distribution of the random variable Z directly (Jaquette, 1973; Bellemare et al., 2017; Mavrin et al., 2019b).”