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.

为了解决深度强化学习算法中的采样效率问题，本文提出了一种新的基于任务调节超网络的检索网络算法，并通过在 MiniGrid 环境下的实验结果验证了其胜于传统算法的性能表现。