Phasic policy gradient
… 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. …
subject to a constraint on the KL-divergence between the original policy and the updated policy. …
Resource Allocation in Time Slotted Channel Hopping (TSCH) networks based on phasic policy gradient reinforcement learning
L Bommisetty, TG Venkatesh�- Internet of Things, 2022 - Elsevier
… 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 …
global optimal solution, we propose a phasic policy gradient reinforcement learning based …
Phasic Policy Gradient Based Resource Allocation for Industrial Internet of Things
L Bommisetty…�- 2022 IEEE 19th Annual�…, 2022 - ieeexplore.ieee.org
… 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 …
-critic policy gradient method that learns the scheduling algorithm in two phases, namely policy …
MAPPG: Multi-Agent Phasic Policy Gradient
Q Zhang, X Zhang, Y Liu, X Zhang…�- 2023 62nd IEEE�…, 2023 - ieeexplore.ieee.org
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 …
agents to further alleviate the non-stationarity of the environment. Different from the existing …
PPG reloaded: an empirical study on what matters in phasic policy gradient
… 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 …
However, through an extensive empirical study, we unveil that policy regularization and …
[HTML][HTML] Spike-based reinforcement learning in continuous state and action space: when policy gradient methods fail
E Vasilaki, N Fr�maux, R Urbanczik…�- PLoS computational�…, 2009 - journals.plos.org
… 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 …
as … We show that in this architecture, a standard policy gradient rule fails to solve the Morris …
Phasic self-imitative reduction for sparse-reward goal-conditioned reinforcement learning
… 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…
samples (both success and failures) to make policy improvement. The SL objective (Eqn…
Correcting discount-factor mismatch in on-policy policy gradient methods
… 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 …
and proximal policy … Our work establishes a more principled policy gradient estimator with …
Model-free policy learning with reward gradients
… 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…
, we develop a new policy gradient estimator—the Reward Policy Gradient (RPG) estimator…
Policy gradient with serial markov chain reasoning
E Cetin, O Celiktutan�- Advances in Neural Information�…, 2022 - proceedings.neurips.cc
… 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 …
algorithm for maximum entropy reinforcement learning (MaxEnt RL) [27, 28], named Steady-State …
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