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 across various tasks within a multitask scenario in 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限于采样效率低下，严重依赖与环境的多次交互来获取准确的决策能力。相比之下，人类依赖海马体从相关任务的历史经验中检索相关信息，这指导他们在学习新任务时的决策过程，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历史经验融入现有强化学习算法，面临两大挑战：第一是选择与当前任务最相关历史经验，第二是将此类经验整合到决策网络中。为解决这些问题，我们提出一种新方法，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检索网络参数，同时通过动态修正机制增强检索网络与决策网络的协同作用。我们在Minigrid环境的多任务场景中对所提方法进行了评估，实验结果表明，本方法显著优于强基线方法。