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Found 3 papers, 1 papers with code
$\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis
Despite policy-based RL methods dominating the literature on RL for program synthesis, the nature of program synthesis tasks hints at a natural alignment with value-based methods.
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs
Reinforcement learning generalizes multi-armed bandit problems with additional difficulties of a longer planning horizon and unknown transition kernel.
Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound
Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e. g., graph classification.
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