Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning
arXiv:2605.31172v1 Announce Type: new Abstract: This work studies the convergence of two-timescale stochastic approximations (SA), a class of iterative algorithms that update two sets of parameters in fast and slow timescales respectively. Notable examples of two-timescale SA in reinforcement learning (RL) include temporal difference learning with gradient correction (TDC) and actor-critic methods. Previously, the stability (i.e., boundedness) and convergence of two-timescale SA were only established under i.i.d. noise. This work instead establishes the stability and convergence of two-timesca
This research provides a theoretical advancement in the stability and convergence of two-timescale stochastic approximations, which are fundamental to developing robust AI algorithms, particularly in reinforcement learning.
Improved theoretical guarantees for two-timescale stochastic approximations will enable more reliable and efficient development of complex AI systems, reducing development costs and increasing performance stability.
The established stability and convergence under more general conditions (Markovian noise instead of i.i.d.) broadens the applicability and reliability of these algorithms in real-world, dynamic environments previously deemed too complex.
- · AI researchers
- · Reinforcement learning developers
- · AI-driven industries
- · Hardware manufacturers for AI
- · Developers relying on less robust SA methods
More stable and efficient reinforcement learning algorithms become available for deployment.
Faster progress in complex AI applications such as robotics, autonomous agents, and adaptive control systems.
Enhanced AI capabilities could accelerate automation across various sectors, impacting labor markets and operational efficiencies on a larger scale.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG