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Found 4 papers, 1 papers with code
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.
Offline Reinforcement Learning for Optimizing Production Bidding Policies
We use a hybrid agent architecture that combines arbitrary base policies with deep neural networks, where only the optimized base policy parameters are eventually deployed, and the neural network part is discarded after training.
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.
Evaluating Online Bandit Exploration In Large-Scale Recommender System
Bandit learning has been an increasingly popular design choice for recommender system.
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