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Found 5 papers, 4 papers with code
Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits
How best to incorporate historical data to "warm start" bandit algorithms is an open question: naively initializing reward estimates using all historical samples can suffer from spurious data and imbalanced data coverage, leading to computational and storage issues $\unicode{x2014}$ particularly salient in continuous action spaces.
Hindsight Learning for MDPs with Exogenous Inputs
Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker.
Adaptive Discretization in Online Reinforcement Learning
In this paper we provide a unified theoretical analysis of tree-based hierarchical partitioning methods for online reinforcement learning, providing model-free and model-based algorithms.
Adaptive Discretization for Model-Based Reinforcement Learning
We introduce the technique of adaptive discretization to design an efficient model-based episodic reinforcement learning algorithm in large (potentially continuous) state-action spaces.
Model-based Reinforcement Learning
reinforcement-learning
+1
Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces
We present an efficient algorithm for model-free episodic reinforcement learning on large (potentially continuous) state-action spaces.
