5 papers
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…
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…
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…
On the Linear Convergence of Policy Gradient under Hadamard Parameterization
Jiacai Liu, Jinchi Chen, Ke Wei
The convergence of deterministic policy gradient under the Hadamard parameterization is studied in the tabular setting and the linear convergence of the algorithm is established. T…