Trace-Mediated Peak Bias: Bridging Temporal Credit Assignment and Cognitive Heuristics in Deep Reinforcement Learning
arXiv:2606.04735v1 Announce Type: new Abstract: Temporal credit assignment is central to both biological and artificial intelligence, yet its interaction with non-linear function approximation is poorly understood. We identify a systematic failure mode in deep reinforcement learning (RL) termed Trace-Mediated Peak Bias (TMPB). At intermediate eligibility trace depths, agents irrationally prefer trajectories with high-magnitude reward ``peaks'' over alternatives with higher cumulative returns. This provides a mechanistic account of the Peak-End Rule: a human memory bias where experiences are ju
This research is emerging as deep reinforcement learning becomes increasingly central to AI development, making its fundamental limitations and biases critically important to understand and address.
Understanding and mitigating 'Trace-Mediated Peak Bias' is crucial for developing more robust, reliable, and rationally behaving AI agents in complex environments.
This paper provides a mechanistic explanation for a specific failure mode in deep RL, which could lead to advancements in AI agent design and training methodologies.
- · AI researchers
- · Reinforcement learning developers
- · Robotics companies
- · AI safety organizations
- · Developers of brittle or unexplainable AI systems
- · AI applications heavily reliant on current deep RL without bias mitigation
AI models will be developed with improved mechanisms to address temporal credit assignment biases.
More reliable and predictable AI agents will emerge in fields like autonomous systems and complex strategy games.
The insights gained may also inform a deeper understanding of cognitive biases in human decision-making, bridging AI and neuroscience.
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Read at arXiv cs.LG