Policy optimization for continuous reinforcement learning
We study reinforcement learning (RL) in the setting of continuous time and space, for an
infinite horizon with a discounted objective and the underlying dynamics driven by a …
infinite horizon with a discounted objective and the underlying dynamics driven by a …
Average-reward reinforcement learning with trust region methods
Most of reinforcement learning algorithms optimize the discounted criterion which is
beneficial to accelerate the convergence and reduce the variance of estimates. Although the …
beneficial to accelerate the convergence and reduce the variance of estimates. Although the …
Processing Network Controls via Deep Reinforcement Learning
M Gluzman - 2022 - search.proquest.com
Novel advanced policy gradient (APG) algorithms, such as proximal policy optimization
(PPO), trust region policy optimization, and their variations, have become the dominant …
(PPO), trust region policy optimization, and their variations, have become the dominant …