Auto-GPT/autogpts/forge/tutorials/wip_006_planning_loop.md

Mastering the Agent Planning Loop: Strategies for Effective Development

Introduction

• Understanding the Agent Planning Loop
• Significance of Effective Planning in Agent Development

Section 1: Concepts of Agent Planning Loop

• The Structure of an Agent Planning Loop
• Key Components and Functions

Section 2: Developing an Effective Planning Strategy

• Setting Goals and Objectives
• Identifying Tasks and Steps within the Planning Loop

Section 3: Implementing the Planning Loop

• Coding the Planning Loop in the Forge Environment
• Utilizing the Agent Protocol APIs

Section 4: Testing and Optimization

• Test-Driven Development of the Planning Loop
• Optimizing the Planning Loop for Better Performance

Section 5: Best Practices

• Tips for Effective Planning Loop Implementation
• Common Pitfalls to Avoid

Conclusion

• Recap of the Tutorial
• Leveraging the Planning Loop for Advanced Agent Development

Additional Resources

From The Rise and Potential of Large Language Model Based Agents: A Survey Zhiheng Xi (Fudan University) et al. arXiv. [paper] [code]

Reasoning

• [2023/05] Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement. Zhiheng Xi (Fudan University) et al. arXiv. [paper] [code]

• [2023-03] Large Language Models are Zero-Shot Reasoners. Takeshi Kojima (The University of Tokyo) et al. arXiv. [paper][code]

• [2023/03] Self-Refine: Iterative Refinement with Self-Feedback. Aman Madaan (Carnegie Mellon University) et al. arXiv. [paper] [code]

• [2022/05] Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. Antonia Creswell (DeepMind) et al. arXiv. [paper]

• [2022/03] Self-Consistency Improves Chain of Thought Reasoning in Language Models. Xuezhi Wang(Google Research) et al. arXiv. [paper] [code]

• [2022/01] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Jason Wei (Google Research,) et al. arXiv. [paper]

Planning

Plan formulation

• [2023/05] Tree of Thoughts: Deliberate Problem Solving with Large Language Models. Shunyu Yao (Princeton University) et al. arXiv. [paper] [code]
• [2023/05] Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents. Yue Wu(Carnegie Mellon University) et al. arXiv. [paper]
• [2023/05] Reasoning with Language Model is Planning with World Model. Shibo Hao (UC San Diego) et al. arXiv. [paper] [code]
• [2023/05] SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks. Bill Yuchen Lin (Allen Institute for Artificial Intelligence) et al. arXiv. [paper] [code]
• [2023/04] LLM+P: Empowering Large Language Models with Optimal Planning Proficiency. Bo Liu (University of Texas at Austin) et al. arXiv. [paper] [code]
• [2023/03] HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face. Yongliang Shen (Microsoft Research Asia) et al. arXiv. [paper] [code]
• [2023/02] Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents. ZiHao Wang (Peking University) et al. arXiv. [paper] [code]
• [2022/05] Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. Denny Zhou (Google Research) et al. arXiv. [paper]
• [2022/05] MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. Ehud Karpas (AI21 Labs) et al. arXiv. [paper]
• [2022/04] Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. Michael Ahn (Robotics at Google) et al. arXiv. [paper]
• [2023/05] Agents: An Open-source Framework for Autonomous Language Agents. Wangchunshu Zhou (AIWaves) et al. arXiv.* [paper] [code]

Plan reflection

• [2023/08] SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning. Ning Miao (University of Oxford) et al. arXiv. [paper] [code]
• [2023/05] ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models. Zhipeng Chen (Renmin University of China) et al. arXiv. [paper] [code]
• [2023/05] Voyager: An Open-Ended Embodied Agent with Large Language Models. Guanzhi Wang (NVIDA) et al. arXiv. [paper] [code]
• [2023/03] Chat with the Environment: Interactive Multimodal Perception Using Large Language Models. Xufeng Zhao (University Hamburg) et al. arXiv. [paper] [code]
• [2022/12] LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models. Chan Hee Song (The Ohio State University) et al. arXiv. [paper] [code]
• [2022/10] ReAct: Synergizing Reasoning and Acting in Language Models. Shunyu Yao ( Princeton University) et al. arXiv. [paper] [code]
• [2022/07] Inner Monologue: Embodied Reasoning through Planning with Language Models. Wenlong Huang (Robotics at Google) et al. arXiv. [paper] [code]
• [2021/10] AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts. Tongshuang Wu (University of Washington) et al. arXiv. [paper]

Appendix

• Example Planning Loop Implementations
• Glossary of Planning Loop Terms