# MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding

arXiv: https://arxiv.org/abs/2002.07408 · HTML: https://mlsanity.com/paper/2002.07408/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Offline Reinforcement Learning for Optimizing Production Bidding Policies](https://mlsanity.com/paper/2310.09426/) (2023)
- [Real-time Bidding Strategy in Display Advertising: An Empirical Analysis](https://mlsanity.com/paper/2212.02222/) (2022)
- [RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems](https://mlsanity.com/paper/2401.01369/) (2023)

## Abstract (arXiv)

Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs. Considering display cost, Return on Investment (ROI), and other influential Key Performance Indicators (KPIs), large ad platforms try to balance the trade-off among various goals in dynamics. To address the challenge, we propose a Multi-ObjecTive Actor-Critics algorithm based on reinforcement learning (RL), named MoTiAC, for the problem of bidding optimization with various goals. In MoTiAC, objective-specific agents update the global network asynchronously with different goals and perspectives, leading to a robust bidding policy. Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments. In addition, we mathematically prove that our model will converge to Pareto optimality. Finally, experiments on a large-scale real-world commercial dataset from Tencent verify the effectiveness of MoTiAC versus a set of recent approaches