This episode explores a research paper that examines how AI can use human-like memory systems to solve problems in partially observable environments. The researchers created "The Rooms Environment," a maze where an AI agent, HumemAI, relies on long-term memory to make decisions, as it can only observe objects in the room it's in. Key features include the use of knowledge graphs to store hidden environment states, and the incorporation of human-inspired memory systems, dividing long-term memory into episodic (event-specific) and semantic (general knowledge). HumemAI learns to manage these memory types through reinforcement learning, outperforming agents that rely solely on observation history. This episode delves into the potential of combining AI with cognitive science to enhance problem-solving in complex environments.
https://arxiv.org/pdf/2408.05861