From the 1 of 7 linked papers with an AI index.
5 papers · 1 filter
On the Policy Convergence of Policy Mirror Descent Methods
Wenye Li, Ke Wei
The paper provides a unified convergence analysis for unregularized policy mirror descent with constant step sizes in finite discounted Markov decision processes, covering a wide r…
Policy Mirror Descent with Temporal Difference Learning: Sample Complexity under Online Markov Data
Wenye Li, Hongxu Chen, Jiacai Liu +1
This paper studies the policy mirror descent (PMD) method, which is a general policy optimization framework in reinforcement learning and can cover a wide range of policy gradient…
On the Convergence of Policy Mirror Descent with Temporal Difference Evaluation
Jiacai Liu, Wenye Li, Ke Wei
Policy mirror descent (PMD) is a general policy optimization framework in reinforcement learning, which can cover a wide range of typical policy optimization methods by specifying…
On the Convergence of Projected Policy Gradient for Any Constant Step Sizes
Jiacai Liu, Wenye Li, Dachao Lin +2
Projected policy gradient (PPG) is a basic policy optimization method in reinforcement learning. Given access to exact policy evaluations, previous studies have established the sub…
Elementary Analysis of Policy Gradient Methods
Jiacai Liu, Wenye Li, Ke Wei
Projected policy gradient under the simplex parameterization, policy gradient and natural policy gradient under the softmax parameterization, are fundamental algorithms in reinforc…