Balancing Exploration and Exploitation in Agent Learning

The Cultural Geography (CG) model is a governmentowned, open-source agent-based model designed to Balancing the ratio of exploration and exploitation is an address the behavioral response of civilian populations in important problem in reinforcement learning [1]. If you conflict environments [3]. Agents within the CG Model examine the relationship between agent and the select their action according to a constant temperature environment in reinforcement learning, agent has two setting over the course of a model run. To enhance the action selections in its environment: exploration and functionality of agents in selecting their actions and to get exploitation. The agent can choose to explore its more realistic results with better utilities we changed this environment and try new actions in search for better ones constant to a dynamic parameter which depends on time in to be adopted in the future, or exploit already tested actions Time Based Selection and on utility in Aggregate Utility and adopt them.