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Found 13 papers, 2 papers with code
An Empirical Study of Deep Reinforcement Learning in Continuing Tasks
In reinforcement learning (RL), continuing tasks refer to tasks where the agent-environment interaction is ongoing and can not be broken down into episodes.
Epinet for Content Cold Start
The exploding popularity of online content and its user base poses an evermore challenging matching problem for modern recommendation systems.
Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank
We study the problem of learning an approximate equilibrium in the offline multi-agent reinforcement learning (MARL) setting.
Uncertainty of Joint Neural Contextual Bandit
However, a major challenge arises when implementing a disjoint neural contextual bandit solution in large-scale recommendation systems, where each item or user may correspond to a separate bandit arm.
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement learning (RL) is a versatile framework for optimizing 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.
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling
Real-world applications of contextual bandits often exhibit non-stationarity due to seasonality, serendipity, and evolving social trends.
Scalable Neural Contextual Bandit for Recommender Systems
In two distinct large-scale experiments with real-world tasks, ENR significantly boosts click-through rates and user ratings by at least 9% and 6% respectively compared to state-of-the-art neural contextual bandit algorithms.
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control
Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline settings where the agent learns from a fixed dataset.
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.
Deep Exploration for Recommendation Systems
Where past work has aimed to learn from subsequent behavior, there has been a lack of effective methods for probing to elicit informative delayed feedback.
Multi-Agent Safe Planning with Gaussian Processes
Multi-agent safe systems have become an increasingly important area of study as we can now easily have multiple AI-powered systems operating together.
