A Bionic Memory Replay Model Based on Hippocampal-Prefrontal Collaboration: Design, Mechanism, and Robot Navigation Verification

The decision-making process in the human brain is not merely a function of existing memory, but an active cognitive process that intricately combines long-term empirical memory with forward-looking prediction. This process is supported by the cooperative interaction between the hippocampus and prefrontal cortex, which enables simulation of future behaviors. The brain simulates potential future behavioral paths and assesses their consequences, adjusting the depth of planning based on environmental structure and task requirements, thereby selecting the optimal decision within a limited time. Based on the hippocampal-prefrontal cooperative planning theory, a memory replay model driven by empirical value and environmental context is proposed. This model combines meta-reinforcement learning with model-based policy optimization techniques by establishing a closed-loop interaction between the hippocampal planning replay network and the prefrontal policy evaluation network. This bionic neural mechanism unifies dynamic programming and strategy optimization, and realizes the dynamic coupling of experience playback and prospective simulation in the process of brain incentive decision-making. Therefore, this framework enhances the agent’s learning ability and improves its learning efficiency in complex environments through “planning”. This paper verifies the proposed model through robot navigation experiments in both static and dynamic environments. The experimental results show that MPR reduces the length of navigation paths by 14%, and its success rate is at least 10% higher than that of other models. Moreover, in the early stage of training, the reward value of MPR was significantly higher than that of other models, and its learning efficiency was nearly twice as high as that of other models. These results verify the improved learning efficiency, convergence speed and adaptability of MPR, making it suitable for robot applications in uncertain and complex environments. Experimental results can be found at: https://github.com/LXDGRA/MPR.