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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.
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.
Hindsight policy gradients
A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy.
