The k-step policy gradient converges exponentially close to the optimal deterministic policy in restricted classes, achieving O(1/T) rates under smoothness assumptions without distribution mismatch factors.
Scalable reinforcement learning for multiagent networked systems.Operations Research, 70(6):3601–3628
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Revisiting Policy Gradients for Restricted Policy Classes: Escaping Myopic Local Optima with $k$-step Policy Gradients
The k-step policy gradient converges exponentially close to the optimal deterministic policy in restricted classes, achieving O(1/T) rates under smoothness assumptions without distribution mismatch factors.