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Found 4 papers, 3 papers with code
Posterior Sampling for Deep Reinforcement Learning
Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies.
Computational Efficiency
Model-based Reinforcement Learning
+2
Hardness in Markov Decision Processes: Theory and Practice
Second, we introduce Colosseum, a pioneering package that enables empirical hardness analysis and implements a principled benchmark composed of environments that are diverse with respect to different measures of hardness.
The Graph Cut Kernel for Ranked Data
Many algorithms for ranked data become computationally intractable as the number of objects grows due to the complex geometric structure induced by rankings.
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits
An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences.
