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 seem to 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 is integrating such experiences into the decision network. To address these challenges, we propose a novel algorithm that utilizes a retrieval network based on a 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 algorithm on the challenging 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制于采样效率低下问题，严重依赖与环境的多次交互才能获得精确的决策能力。相比之下，人类似乎依赖海马体从相关任务的过往经验中检索相关信息，在学习新任务时指导决策，而非单纯依赖环境交互。然而，为智能体设计类似海马体的模块以将过往经验融入现有强化学习算法面临两大挑战：一是如何选择与当前任务最相关的过往经验，二是如何将这些经验整合到决策网络中。针对这些挑战，我们提出了一种新型算法，该算法采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。我们在具有挑战性的MiniGrid环境中对该算法进行评估。实验结果表明，我们提出的方法显著优于强基线模型。