  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
