Trace-Mediated Peak Bias: Bridging Temporal Credit Assignment and Cognitive Heuristics in Deep Reinforcement Learning

Researchers identify Trace-Mediated Peak Bias (TMPB), a systematic failure in deep reinforcement learning where agents irrationally prioritize high-magnitude reward spikes over trajectories with greater cumulative returns. This phenomenon mirrors the human Peak-End Rule cognitive bias and reveals how mathematical constraints in credit assignment systems naturally produce human-like value distortions, with adaptive optimizers offering a potential solution.

This research addresses a fundamental disconnect between how artificial and biological intelligence assign credit for outcomes over time. The discovery of Trace-Mediated Peak Bias reveals that deep RL agents systematically overvalue extreme reward moments when using eligibility traces at intermediate depths, creating a mechanistic explanation for why human memory weights vivid experiences disproportionately. The finding bridges computational neuroscience and machine learning by demonstrating that irrational preferences emerge not from evolutionary quirks but from mathematical inevitabilities in distributed credit assignment systems.

The paper identifies the root cause: eligibility traces amplify distal temporal difference errors into gradient shocks that standard fixed-step-size optimizers cannot adequately normalize, producing global overestimation biases. This mathematical pathology has significant implications for AI safety and alignment, as it suggests that cognitive heuristics humans exhibit may spontaneously appear in sufficiently complex learning systems. The research demonstrates that adaptive optimization methods—which use second-moment normalization—mitigate this bias more effectively than standard SGD approaches.

For the broader AI development community, this work challenges assumptions about scaling laws and optimizer choice. It suggests that algorithm selection profoundly impacts not just convergence speed but fundamental value alignment properties. The findings could influence how researchers design RL systems for critical applications where rational decision-making is essential, particularly in domains like autonomous systems and financial decision-making where peak events versus cumulative outcomes carry real consequences.

• →Trace-Mediated Peak Bias causes deep RL agents to irrationally prefer high-reward spikes over higher cumulative returns, mirroring human memory biases.
• →The bias emerges from mathematical constraints in credit assignment rather than design choices, suggesting cognitive heuristics may be unavoidable in complex learning systems.
• →Eligibility traces amplify temporal difference errors into gradient shocks that fixed-step-size optimizers cannot normalize, causing global overestimation.
• →Adaptive optimization methods using second-moment normalization effectively mitigate this pathology better than standard SGD.
• →The research has implications for AI safety, optimizer selection, and understanding why seemingly irrational behaviors appear in advanced learning systems.