Global linear convergence of entropy-regularized softmax policy gradient beyond tabular MDPs
arXiv:2605.24939v1 Announce Type: new Abstract: We study the global convergence of policy gradient for infinite-horizon entropy-regularized Markov decision processes (MDPs) with continuous state and action spaces. We consider log-linear softmax policies with linear function approximation, which extend the tabular softmax parameterization while retaining a tractable policy class. Under $Q^\pi_\tau$-realizability for the regularized state-action value function, we first establish a non-uniform Polyak--{\L}ojasiewicz (P\L) inequality. The non-uniformity arises through degeneracy of constants asso
This research contributes to the ongoing theoretical advancements in reinforcement learning, specifically addressing global convergence challenges in more complex policy gradient settings.
Improved theoretical understanding of policy gradient methods, especially with continuous state/action spaces, is crucial for developing more robust and generalizable AI agents.
The established global linear convergence provides a stronger theoretical foundation for applying entropy-regularized softmax policies in complex, non-tabular environments, potentially accelerating agent development.
- · AI researchers
- · Reinforcement learning developers
- · Companies building advanced AI agents
More efficient and reliable training of AI agents in complex, real-world environments.
Acceleration of AI agent deployment in fields requiring sophisticated control and decision-making.
Potentially enables new applications for AI agents that were previously intractable due to convergence challenges.
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Read at arXiv cs.LG