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Found 6 papers, 1 papers with code
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
We then build a REINFORCE recommender agent, coined EcoAgent, to optimize a joint objective of user utility and the counterfactual utility lift of the provider associated with the recommended content, which we show to be equivalent to maximizing overall user utility and the utilities of all providers on the platform under some mild assumptions.
Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition
Transformer-based models have achieved stateof-the-art results in many tasks in natural language processing.
ConQUR: Mitigating Delusional Bias in Deep Q-learning
Delusional bias is a fundamental source of error in approximate Q-learning.
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments.
BRPO: Batch Residual Policy Optimization
In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e. g., by constraining the learned action distribution to differ from the behavior policy by some maximum degree that is the same at each state.
Advantage Amplification in Slowly Evolving Latent-State Environments
Latent-state environments with long horizons, such as those faced by recommender systems, pose significant challenges for reinforcement learning (RL).
