… Specifically, we set the policy gradient loss (… gradient is the policy gradient estimator,
subject to a constraint on the KL-divergence between the original policy and the updated policy. …

… Motivated by these gaps in the literature to design a scheduling algorithm that achieves
global optimal solution, we propose a phasic policy gradient reinforcement learning based …

… In this paper, we propose a phasic policy gradient (PPG) based TSCH schedule learning …
-critic policy gradient method that learns the scheduling algorithm in two phases, namely policy …

We propose a Multi-Agent Phasic Policy Gradient (MAPPG) algorithm, which can assist
agents to further alleviate the non-stationarity of the environment. Different from the existing …

… In model-free reinforcement learning, recent methods based on a phasic policy gradient (PPG) …
However, through an extensive empirical study, we unveil that policy regularization and …

… The family of learning rules includes an optimal rule derived from policy gradient methods
as … We show that in this architecture, a standard policy gradient rule fails to solve the Morris …

… The RL objective is policy gradient over rollout data (Eqn. 1), which requires (primarily) on-policy
samples (both success and failures) to make policy improvement. The SL objective (Eqn…

… other on-policy policy gradient estimators, including batch actor-critic (Konda & Tsitsiklis 1999)
and proximal policy … Our work establishes a more principled policy gradient estimator with …

… Reward Policy Gradient estimator, a novel approach that integrates reward gradients without …
, we develop a new policy gradient estimator—the Reward Policy Gradient (RPG) estimator…

… method to estimate the policy gradient. Hence, we implement a new effective off-policy
algorithm for maximum entropy reinforcement learning (MaxEnt RL) [27, 28], named Steady-State …