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Found 10 papers, 2 papers with code
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of recommendations on user behavior.
AMOM: Adaptive Masking over Masking for Conditional Masked Language Model
Experiments on \textbf{3} different tasks (neural machine translation, summarization, and code generation) with \textbf{15} datasets in total confirm that our proposed simple method achieves significant performance improvement over the strong CMLM model.
Online Sparse Streaming Feature Selection Using Adapted Classification
Traditional feature selections need to know the feature space before learning, and online streaming feature selection (OSFS) is proposed to process streaming features on the fly.
A Validation Tool for Designing Reinforcement Learning Environments
Reinforcement learning (RL) has gained increasing attraction in the academia and tech industry with launches to a variety of impactful applications and products.
Semantic Parsing Natural Language into Relational Algebra
Natural interface to database (NLIDB) has been researched a lot during the past decades.
Dual Monte Carlo Tree Search
This technique is applicable to any MCTS based algorithm to reduce the number of updates to the tree.
Solving QSAT problems with neural MCTS
After training, an off-the-shelf QSAT solver is used to evaluate the performance of the algorithm.
First-Order Problem Solving through Neural MCTS based Reinforcement Learning
We propose a general framework, Persephone, to map the FOL description of a combinatorial problem to a semantic game so that it can be solved through a neural MCTS based reinforcement learning algorithm.
Learning Self-Game-Play Agents for Combinatorial Optimization Problems
Recent progress in reinforcement learning (RL) using self-game-play has shown remarkable performance on several board games (e. g., Chess and Go) as well as video games (e. g., Atari games and Dota2).
