A Hybrid Algorithm in Reinforcement Learning for Crowd Simulation
1K.Pavithra, Research Scholar, School of IT and Science, Dr.G.R.D College of Science, Coimbatore.
2G.Radhamani, Professor & Director, School of IT and Science, Dr.G.R.D College of Science, Coimbatore.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5251-5255 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9187038620/2020©BEIESP | DOI: 10.35940/ijrte.F9187.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Exploiting the efficiency and stability of Dynamic Crowd, the paper proposes a hybrid crowd simulation algorithm that runs using multi agents and it mainly focuses on identifying the crowd to simulate. An efficient measurement for both static and dynamic crowd simulation is applied in tracking and transportation applications. The proposed Hybrid Agent Reinforcement Learning (HARL) algorithm combines the Q-Learning off-policy value function and SARSA algorithm on-policy value function, which is used for dynamic crowd evacuation scenario. The HARL algorithm performs multiple value functions and combines the policy value function derived from the multi agent to improve the performance. In addition, the efficiency of the HARL algorithm is able to demonstrate in varied crowd sizes. Two kinds of applications are used in Reinforcement Learning such as tracking applications and transportation monitoring applications for pretending the crowd sizes.
Keywords: Artificial Intelligence, Reinforcement Learning, Reinforcement Learning Agent, Crowd Simulation.
Scope of the Article: Deep learning.