Added planning papers

autogpts/forge/tutorials/004_planning_loop.md

@@ -29,8 +29,51 @@
 - Leveraging the Planning Loop for Advanced Agent Development
 
 ## Additional Resources
-- Links to Documentation and Tutorials on Planning Loop
-- Community Discussions and Tips
+
+From **The Rise and Potential of Large Language Model Based Agents: A Survey** *Zhiheng Xi (Fudan University) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.14497)] [[code](https://github.com/woooodyy/llm-agent-paper-list)]
+
+### Reasoning
+
+- [2023/05] **Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement.** *Zhiheng Xi (Fudan University) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.14497)] [[code](https://github.com/woooodyy/self-polish)]
+
+- [2023-03] **Large Language Models are Zero-Shot Reasoners.** *Takeshi Kojima (The University of Tokyo) et al. arXiv.* [[paper](https://arxiv.org/abs/2205.11916)][[code](https://github.com/kojima-takeshi188/zero_shot_cot)]
+
+- [2023/03] **Self-Refine: Iterative Refinement with Self-Feedback.** *Aman Madaan (Carnegie Mellon University) et al. arXiv.* [[paper](https://arxiv.org/abs/2303.17651)] [[code](https://github.com/madaan/self-refine)]
+
+- [2022/05] **Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.** *Antonia Creswell (DeepMind) et al. arXiv.* [[paper](https://arxiv.org/abs/2205.09712)]
+
+- [2022/03] **Self-Consistency Improves Chain of Thought Reasoning in Language Models.** *Xuezhi Wang(Google Research) et al. arXiv.* [[paper](https://arxiv.org/abs/2203.11171)] [[code](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bart)]
+
+- [2022/01] **Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.** *Jason Wei (Google Research,) et al. arXiv.* [[paper](https://arxiv.org/abs/2201.11903)] 
+
+
+### Planning
+
+#### Plan formulation
+
+- [2023/05] **Tree of Thoughts: Deliberate Problem Solving with Large Language Models.** *Shunyu Yao (Princeton University) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.10601)] [[code](https://github.com/princeton-nlp/tree-of-thought-llm)]
+- [2023/05] **Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents.** *Yue Wu(Carnegie Mellon University) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.02412)]
+- [2023/05] **Reasoning with Language Model is Planning with World Model.** *Shibo Hao (UC San Diego) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.14992)] [[code](https://github.com/Ber666/RAP)]
+- [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](https://arxiv.org/abs/2305.17390)] [[code](https://github.com/yuchenlin/swiftsage)]
+- [2023/04] **LLM+P: Empowering Large Language Models with Optimal Planning Proficiency.** *Bo Liu (University of Texas at Austin) et al. arXiv.* [[paper](https://arxiv.org/abs/2304.11477)] [[code](https://github.com/Cranial-XIX/llm-pddl)]
+- [2023/03] **HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face.** *Yongliang Shen (Microsoft Research Asia) et al. arXiv.* [[paper](https://arxiv.org/abs/2303.17580)] [[code](https://github.com/microsoft/JARVIS)]
+- [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](https://arxiv.org/abs/2302.01560)] [[code](https://github.com/CraftJarvis/MC-Planner)]
+- [2022/05] **Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.** *Denny Zhou (Google Research) et al. arXiv.* [[paper](https://arxiv.org/abs/2205.10625)] 
+- [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](https://arxiv.org/abs/2205.00445)] 
+- [2022/04] **Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.** *Michael Ahn (Robotics at Google) et al. arXiv.* [[paper](https://arxiv.org/abs/2204.01691)]
+- [2023/05] **Agents: An Open-source Framework for Autonomous Language Agents.** Wangchunshu Zhou (AIWaves) et al. arXiv.* [[paper](https://arxiv.org/pdf/2309.07870.pdf)] [[code](https://github.com/aiwaves-cn/agents)]
+
+
+#### 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](https://arxiv.org/abs/2308.00436)] [[code](https://github.com/NingMiao/SelfCheck)]
+- [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](https://arxiv.org/abs/2305.14323)] [[code](https://github.com/RUCAIBOX/ChatCoT)]
+- [2023/05] **Voyager: An Open-Ended Embodied Agent with Large Language Models.** *Guanzhi Wang (NVIDA) et al. arXiv.* [[paper](https://arxiv.org/abs/2305.16291)] [[code](https://voyager.minedojo.org/)]
+- [2023/03] **Chat with the Environment: Interactive Multimodal Perception Using Large Language Models.** *Xufeng Zhao (University Hamburg) et al. arXiv.* [[paper](https://arxiv.org/abs/2303.08268)] [[code](https://matcha-model.github.io/)]
+- [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](https://arxiv.org/abs/2212.04088)] [[code](https://dki-lab.github.io/LLM-Planner/)]
+- [2022/10] **ReAct: Synergizing Reasoning and Acting in Language Models.** *Shunyu Yao ( Princeton University) et al. arXiv.* [[paper](https://arxiv.org/abs/2210.03629)] [[code](https://react-lm.github.io/)]
+- [2022/07] **Inner Monologue: Embodied Reasoning through Planning with Language Models.** *Wenlong Huang (Robotics at Google) et al. arXiv.* [[paper](https://arxiv.org/abs/2207.05608)] [[code](https://innermonologue.github.io/)]
+- [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](https://arxiv.org/abs/2110.01691)]
 
 ## Appendix
 - Example Planning Loop Implementations