Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds

50 citations recorded by the source corpus.

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Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning (2017, background): “Previous work has examined safe exploration in small MDPs. Moldovan & Abbeel (2012a) examine risk-sensitive objectives for MDPs, and propose a new objective of which minmax and expectation optimization are both special cases.”
A Review of Safe Reinforcement Learning: Methods, Theory and Applications (2022, background): “For example, Moldovan and Pieter use the Chernoff function (10) in [193] to achieve near-optimal bounds with desirable theoretical properties.”
A Review of Safe Reinforcement Learning: Methods, Theories, and Applications (2024, unknown): “For example, Moldovan and Abbeel use the Chernoff function (4) in [12] to achieve near-optimal bounds with desirable theoretical properties.”
A comprehensive survey on safe reinforcement learning (2015, methodology): “These conclusions can also be obtained for Safe RL from Table 2 where most of the approaches address finite MDPs (Heger, 1994a; Gaskett, 2003; Moldovan and Abbeel, 2012a) and problems with continuous or large state spaces (Mihatsch and Neuneier, 2002; Geibel and Wysotzki, 2005; Thomaz and Breazeal,…”