arXiv:2605.30160v1 Announce Type: new Abstract: Chaotic dynamical systems pose a fundamental challenge for Reinforcement Learning (RL): exponential sensitivity to initial conditions induces high-variance bootstrap targets and poorly conditioned gradient updates. Chaotic dynamics arise across scientific and engineering domains, from fluid flows and climate systems to multi-agent systems, where reliable learning is highly desirable. Standard RL methods optimise expected returns through scalar value functions, implicitly averaging over diverging trajectories and entangling trajectory level instab
This research addresses a long-standing challenge in applying reinforcement learning to complex, real-world systems, enabled by advancements in AI theory and computational power.
Improved robustness of RL in chaotic systems could unlock autonomous decision-making in highly dynamic environments, from climate modeling to advanced robotics and multi-agent control.
The ability to reliably train RL agents in chaotic systems moves the field closer to deploying AI in traditionally intractable domains by addressing fundamental instability issues.
- · AI research institutions
- · Robotics industry
- · Climate scientists
- · Autonomous systems developers
- · Developers of unstable control systems
- · Legacy simulation platforms
Reinforcement learning becomes applicable to a wider array of real-world problems characterized by non-linear and unpredictable dynamics.
This could accelerate the development of highly resilient AI agents capable of operating in complex and rapidly changing environments.
Advances in understanding and controlling chaotic systems via AI may lead to breakthroughs in fields like weather prediction and complex industrial process optimization.
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Read at arXiv cs.LG