Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork

Understand

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.