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Found 3 papers, 2 papers with code
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
We propose a new method for count-based exploration in high-dimensional state spaces.
Coarse-Grained Smoothness for RL in Metric Spaces
We propose a new coarse-grained smoothness definition that generalizes the notion of Lipschitz continuity, is more widely applicable, and allows us to compute significantly tighter bounds on Q-functions, leading to improved learning.
Towards Amortized Ranking-Critical Training for Collaborative Filtering
In this paper we investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to directly optimize the non-differentiable quality metrics of interest.
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