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TheSequence
Edge 403: Memory-Based Planning and Autonomous Agents

Edge 403: Memory-Based Planning and Autonomous Agents

Supplying agents with an external memory to execute more complex plans.

Jun 11, 2024
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TheSequence
TheSequence
Edge 403: Memory-Based Planning and Autonomous Agents
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An artistic representation of an artificial intelligence system using a large memory to plan and solve a complex problem in multiple steps. The image features a futuristic, high-tech environment with a massive, glowing data storage unit at the center, connected to various parts of the system through intricate, glowing pathways. The AI system is visualized as a holographic interface displaying multiple layers of data and steps of the plan. Around the central memory unit, robotic arms or digital interfaces interact with the data, representing the multiple steps and complex problem-solving process. The background includes abstract representations of complex problems such as mathematical equations, circuit diagrams, and network connections.
Created Using DALL-E

In this Issue:

  1. An introduction to memory-based planning in autonomous agents.

  2. Understanding the TravelPlanner benchmark for planning in autonomous agents.

  3. A review of the amazing MemGPT framework

💡 ML Concept of the Day: Memory-Based Planning Methods in Autonomous Agents

The last type of planning method that we will cover in this series about autonomous agents are memory based planning. As it name indicates, the idea of these methods is to use an external memory source as a way to enhance the planning capabilities of agents. Two primary methods currently exist to improve these planning capabilities through the use of memory: RAG-based memory and embodied memory.

1)      RAG-based Memory

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