Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 11 Issue 7
Implementation and Evaluation of Advantage Actor-Critic Algorithm on a Desktop Computer with a Multi-Core CPU
Fredy Martínez, Angélica Rendón
10.7753/IJCATR1107.1005
keywords : A2C; agent; cartpole-v0; environment; optimal policy; reinforcement learning; value function
In this paper, the implementation and evaluation of the Advantage Actor-Critic (A2C) algorithm, one of the most important Deep Reinforcement Learning schemes, is performed. The objective is to determine the behavior of the algorithm on a desktop computer with a multi-core CPU, establishing its behavior, performance, and resource consumption for embedded applications. This algorithm makes use of multiple agents on parallel instances of the environment so that each agent adds knowledge to the system, which is weighted by a value of Advantage that evaluates its interaction in the environment. This assessment is performed on OpenAI's CartPole-v0 playground, so the results are comparable and easily reproducible. The results show a high performance of the algorithm for different instances with fixed-length segments of experience, which allows us to think of successful use on more resource-constrained hardware platforms.
@artical{1172022ijcatr11071005,
Title = "Implementation and Evaluation of Advantage Actor-Critic Algorithm on a Desktop Computer with a Multi-Core CPU",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "11",
Issue ="7",
Pages ="284 - 290",
Year = "2022",
Authors =" Fredy Martínez, Angélica Rendón"}
The paper develops an evaluation of the A2C algorithm on multi-core CPUs.
The paper performs a sensitivity check of algorithm parameters.
The paper gives a complete description of the A2C algorithm.
Complete detail of the code used is given.