Reinforcement Learning
"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
Accelerating Value Iteration with Anchoring
Neural Information Processing Systems
In this paper, we present the first accelerated VI for both the Bellman consistency and optimality operators.
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Accelerating Value Iteration with Anchoring Jongmin Lee 1 Ernest K. Ryu
Neural Information Processing Systems
In this paper, we present the first accelerated VI for both the Bellman consistency and optimality operators.
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IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL *Pascal Leroy
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In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition.
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Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift
Neural Information Processing Systems
While performing favourably on the independent and identically distributed (i.i.d.)
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