Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
arXiv:2109.08331
Abstract
In recommender systems (RecSys) and real-time bidding (RTB) for online advertisements, we often try to optimize sequential decision making using bandit and reinforcement learning (RL) techniques. In these applications, offline reinforcement learning (offline RL) and off-policy evaluation (OPE) are beneficial because they enable safe policy optimization using only logged data without any risky online interaction. In this position paper, we explore the potential of using simulation to accelerate practical research of offline RL and OPE, particularly in RecSys and RTB. Specifically, we discuss how simulation can help us conduct empirical research of offline RL and OPE. We take a position to argue that we should effectively use simulations in the empirical research of offline RL and OPE. To refute the counterclaim that experiments using only real-world data are preferable, we first point out the underlying risks and reproducibility issue in real-world experiments. Then, we describe how these issues can be addressed by using simulations. Moreover, we show how to incorporate the benefits of both real-world and simulation-based experiments to defend our position. Finally, we also present an open challenge to further facilitate practical research of offline RL and OPE in RecSys and RTB, with respect to public simulation platforms. As a possible solution for the issue, we show our ongoing open source project and its potential use case. We believe that building and utilizing simulation-based evaluation platforms for offline RL and OPE will be of great interest and relevance for the RecSys and RTB community.
SimuRec workshop at RecSys2021
References in corpus (9)
- Benchmarking Batch Deep Reinforcement Learning Algorithms
- Learning from Logged Implicit Exploration Data
- RecSim: A Configurable Simulation Platform for Recommender Systems
- Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
- NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
- Evaluating the Robustness of Off-Policy Evaluation
- Representation Matters: Offline Pretraining for Sequential Decision Making
- Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
- Regularized Behavior Value Estimation