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In reinforcement learning, the classic objectives of maximizing discounted and finite-horizon cumulative rewards are PAC-learnable: There are algorithms that learn a near-optimal policy with high probability using a finite amount of samples and compu
Externí odkaz:
http://arxiv.org/abs/2303.05518
In recent years, researchers have made significant progress in devising reinforcement-learning algorithms for optimizing linear temporal logic (LTL) objectives and LTL-like objectives. Despite these advancements, there are fundamental limitations to
Externí odkaz:
http://arxiv.org/abs/2111.12679
Publikováno v:
Proc. ACM Program. Lang. 5, POPL, Article 20 (2021)
A Reduction -- an accumulation over a set of values, using an associative and commutative operator -- is a common computation in many numerical computations, including scientific computations, machine learning, computer vision, and financial analytic
Externí odkaz:
http://arxiv.org/abs/2007.11203
Researchers have recently proposed several systems that ease the process of performing Bayesian probabilistic inference. These include systems for automatic inference algorithm synthesis as well as stronger abstractions for manual algorithm developme
Externí odkaz:
http://arxiv.org/abs/1805.01863