Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-making capabilities. In contrast, humans rely on their hippocampus to retrieve relevant information from past experiences of relevant tasks, which guides their decision-making when learning a new task, rather than exclusively depending on environmental interactions. Nevertheless, designing a hippocampus-like module for an agent to incorporate past experiences into established reinforcement learning algorithms presents two challenges. The first challenge involves selecting the most relevant past experiences for the current task, and the second challenge is integrating such experiences into the decision network. To address these challenges, we propose a novel method that utilizes a retrieval network based on task-conditioned hypernetwork, which adapts the retrieval network's parameters depending on the task. At the same time, a dynamic modification mechanism enhances the collaborative efforts between the retrieval and decision networks. We evaluate the proposed method on the MiniGrid environment.The experimental results demonstrate that our proposed method significantly outperforms strong baselines.

翻译：深度强化学习算法通常受采样效率低下的困扰，严重依赖与环境的多次交互才能获得准确的决策能力。而人类在习得新任务时，会依赖海马体从过往相关任务的经验中检索相关信息来指导决策，而非完全依赖环境交互。然而，为智能体设计类似海马体的模块，以将其过往经验融入现有强化学习算法，面临两大挑战：其一是如何为当前任务筛选最相关的过往经验，其二是如何将这些经验整合至决策网络。针对上述挑战，本文提出一种新方法，该方法利用基于任务条件超网络的检索网络，根据任务自适应调整网络参数；同时，动态修改机制增强了检索网络与决策网络的协同作用。我们在MiniGrid环境中评估了所提方法。实验结果表明，该方法显著优于强基线模型。