Global Optimality for Constrained Exploration via Penalty Regularization
Efficient exploration is a central problem in reinforcement learning and is often formalized as maximizing the entropy of the state-action occupancy measure. While unconstrained maximum-entropy exploration is relatively well understood, real-world exploration is often constrained by safety, resource, or imitation requirements. This constrained setting is particularly challenging because entropy maximization lacks additive structure, rendering Bellman-equation-based methods inapplicable. Moreover, scalable approaches require policy parameterization, inducing non-convexity in both the objective and the constraints. To our knowledge, the only prior model-free policy-gradient approach for thi...
| Authors | Florian Wolf, Ilyas Fatkhullin, Niao He |
|---|---|
| Primary category | cs.LG |
| Published | 30 April 2026 |
| Last updated at source | 28 September 2026 |
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