In constrained Markov decision processes, enforcing constraints during training is often thought of as decreasing the final return. Recently, it was shown that constraints can be incorporated directly in the policy geometry, yielding an optimization trajectory close to the central path of a barrier method, which does not compromise final return. Building on this idea, we introduce Central Path Proximal Policy Optimization (C3PO), a simple modification of PPO that produces policy iterates, which stay close to the central path of the constrained optimization problem. Compared to existing on-policy methods, C3PO delivers improved performance with tighter constraint enforcement, suggesting that central path-guided updates offer a promising direction for constrained policy optimization.
View on arXiv@article{milosevic2025_2506.00700,
title={ Central Path Proximal Policy Optimization },
author={ Nikola Milosevic and Johannes Müller and Nico Scherf },
journal={arXiv preprint arXiv:2506.00700},
year={ 2025 }
}