Forest-Inspired Reinforcement Learning Based On Nature Ecosystem Feedback Mechanisms

This study introduces the Forest-Inspired Reinforcement Learning (FIRL) algorithm, a novel approach that harnesses the intricate feedback mechanisms observed in forest ecosystems. A multiagent RL system is proposed, where agents maintain mutualistic relationships, exchanging rewards or insights, fostering a cooperative learning environment. The learning process undergoes stages, similar to ecological succession in forests. The initial stages prioritize exploration, while the mature stages emphasize exploitation and refinement. The algorithm incorporates mechanisms to recover from suboptimal decisions, drawing inspiration from a forest’s ability to regenerate post disturbances. A dual agent system, inspired by predator-prey dynamics, ensures a balance between exploration and exploitation in the learning process.