Agents are always in an interactive environment. With time, the intelligence of agents will be affected by the interactive environment. Agents need to coordinate the interaction with different environmental factors to achieve the optimal intelligence state. We consider an agent’s interaction with the environment as an action-reward process. An agent balances the reward it receives by acting with various environmental factors. This paper refers to the concept of interaction between an agent and the environment in reinforcement learning and calculates the optimal mode of interaction between an agent and the environment. It aims to help agents maintain the best intelligence state as far as possible. For specific interaction scenarios, this paper takes food collocation as an example, the evolution process between an agent and the environment is constructed, and the advantages and disadvantages of the evolutionary environment are reflected by the evolution status of the agent. Our practical case study using dietary combinations demonstrates the feasibility of this interactive balance.
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This work was supported by the National Natural Science Foundation of China (No. 62072440) and the Beijing Natural Science Foundation (No. 4202072).
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