Zero Collapse: A Failure Mode of Policy Gradient Methods in Discontinuous Reward Environments
arXiv:2605.30896v1 Announce Type: new Abstract: Bidding in repeated auctions is a central challenge for reinforcement learning (RL), combining continuous control with the strategic complexities of digital advertising. While policy gradient and value-based methods seem well-suited for these settings, they often struggle with the discontinuous, "cliff-like" nature of auction reward landscapes. In a first-price auction, for example, a bidder receives zero reward until they cross a specific threshold, after which the reward decreases as the bid increases. This creates a landscape of flat, zero-rew
This research addresses a fundamental limitation in reinforcement learning algorithms that is becoming more apparent as AI is deployed in complex real-world environments like financial markets.
Understanding and mitigating 'Zero Collapse' in policy gradient methods is crucial for building robust and reliable AI agents capable of operating effectively in environments with discontinuous rewards, such as auctions or other strategic economic settings.
The identification of this failure mode and potential solutions could lead to more stable and performant AI agents in specific high-stakes commercial applications, rather than a fundamental change in the overall AI landscape.
- · AI researchers in RL
- · Companies using RL for bidding/trading
- · Developers of robust AI agents
- · Companies prematurely deploying RL in discontinuous environments
- · First-price auction participants without sophisticated RL
Policy gradient methods will be refined to better handle discontinuous reward functions.
Improved RL agents will achieve higher efficiencies and profits in digital advertising and financial markets.
The application scope of reinforcement learning will expand into more complex, real-world strategic decision-making domains.
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Read at arXiv cs.LG