arXiv:2605.20996v1 Announce Type: new Abstract: Most value-based and actor--critic reinforcement learning methods rely on Bellman-style recursions, yet these recursions collapse under non-exponential discounting common in human preferences and survival processes. We show the breakdown is structural: exponential discounting sits at a fragile intersection of multiplicativity and time homogeneity, and violating either property breaks standard dynamic programming. To overcome this, we propose Pontryagin-Guided Direct Policy Optimization (PG-DPO), a variational framework that abandons recursion and
This research addresses a fundamental limitation in current reinforcement learning approaches, which have seen rapid advancements but struggle with human-like discounting, as AI systems are increasingly deployed in real-world human-centric environments.
A strategic reader should care because overcoming the limitations of exponential discounting is crucial for developing AI agents that can make more human-aligned decisions, particularly in complex, long-term planning scenarios.
This research introduces a new computational framework, Pontryagin-Guided Direct Policy Optimization (PG-DPO), allowing AI systems to handle non-exponential discounting, moving beyond the traditional Bellman recursions.
- · AI researchers
- · Reinforcement learning platforms
- · Ethical AI development
- · Robotics
- · AI systems limited to exponential discounting
- · Traditional Bellman recursion-centric RL algorithms
AI agents will be able to learn and optimize for more complex and human-like preference structures, improving their applicability in fields like economics or social simulation.
This could lead to more robust and less 'brittle' AI systems in finance, resource management, or personal assistant roles, as their decision logic moves closer to human intuition regarding long-term value.
The ability of AI to model nuanced human decision-making with non-exponential discounting could accelerate the development of sophisticated AI agents capable of truly autonomous, high-level planning that aligns with complex organizational or societal goals.
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Read at arXiv cs.LG