Deciding when to quit the gambler's ruin game with unknown probabilities
In the standard definition of the classical gambler's ruin game, a persistent player enters in a
stochastic process with an initial budget b 0, which is, round after round, either increased by …
stochastic process with an initial budget b 0, which is, round after round, either increased by …
Modeling Risk in Reinforcement Learning: A Literature Mapping
Safe reinforcement learning deals with mitigating or avoiding unsafe situations by
reinforcement learning (RL) agents. Safe RL approaches are based on specific risk …
reinforcement learning (RL) agents. Safe RL approaches are based on specific risk …
Approaching Single-Episode Survival Reinforcement Learning with Safety-Threshold Q-Learning
FS Perotto, M Nargeot, A Ouahbi - International Conference on …, 2024 - hal.science
Survival Reinforcement Learning is a specific type of RL problem constrained by a risk of
ruin. The underlying stochastic sequential decision process with which the agent interacts …
ruin. The underlying stochastic sequential decision process with which the agent interacts …
Survival Multiarmed Bandits with Boostrapping Methods
P Veroutis, F Godin - arXiv preprint arXiv:2410.16486, 2024 - arxiv.org
The Multiarmed Bandits (MAB) problem has been extensively studied and has seen many
practical applications in a variety of fields. The Survival Multiarmed Bandits (S-MAB) open …
practical applications in a variety of fields. The Survival Multiarmed Bandits (S-MAB) open …
Time is Budget: A Heuristic for Reducing the Risk of Ruin in Multi-armed Gambler Bandits
In this paper we consider Multi-Armed Gambler Bandits (MAGB), a stochastic random
process in which an agent performs successive actions and either loses 1 unit from its …
process in which an agent performs successive actions and either loses 1 unit from its …