3 papers
cs.AI2026
Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning
Ubayd Ali Bapoo, Clement N Nyirenda
Parameterized action reinforcement learning has shown strong performance in environments requiring both discrete action selection and continuous parameterization. Prior work establ…
cs.LG2025
Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
Ubayd Bapoo, Clement N Nyirenda
This study evaluates the performance of Soft Actor Critic (SAC), Greedy Actor Critic (GAC), and Truncated Quantile Critics (TQC) in high-dimensional decision-making tasks using ful…
cs.NE2020
A Comparative Evaluation of Population-based Optimization Algorithms for Workflow Scheduling in Cloud-Fog Environments
Dineshan Subramoney, Clement N. Nyirenda
This work presents a comparative evaluation of four population-based optimization algorithms for workflow scheduling in cloud-fog environments. These algorithms are as follows: Par…