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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A collection of recent papers on building autonomous agent. Two topics included: RL-based / LLM-based agents.
| Date | Stars |
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| 2026-07-31 | 751 |
| 2026-08-03 | 753 |
| 2026-08-06 | 753 |
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 # Awesome-Papers-Autonomous-Agent This is a collection of recent papers focusing on autonomous agent. Here is how Wikipedia defines Agent: > In artificial intelligence, an intelligent agent is an agent acting in an intelligent manner; It perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostator other control systemis considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome. Thus, the key of an agent is that it can <i>achieve goals</i>, <i>acquire knowledge</i> and <i>continually improve</i>. The traditional agents in RL research will not be considered in this collection. Though LLM-based agents have caught people's eyes in recent research, RL-based agents also take their special position. Specifically, this repo is interested in two types of agent: RL-based agent and LLM-based agent. Note that this paper list is **under active maintaince**. Free free to open an issue if you found any missed papers that fit the topic. ------ ## Update history - 2024/01/31: Add a special list for surveys on autonomous agent. - 2023/12/08: Add papers accepted by [ICML'23](https://icml.cc/virtual/2023/papers.html?filter=titles) and [ICLR'23]() :rocket: - 2023/11/08: Add papers accepted by [NeurIPS'23](https://openreview.net/group?id=NeurIPS.cc/2023/Conference&referrer=%5BHomepage%5D(%2F)). Add related links (project page or github) to these accepted papers :tada: - 2023/10/25: Classify all papers based on their research topics. Check ToC for the standard of classification :clap: - 2023/10/18: Release first version of collection, including papers submitted to [ICLR 2024](https://openreview.net/group?id=ICLR.cc/2024/Conference) :rocket: <!-- START doctoc generated TOC please keep comment here to allow auto update --> <!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE --> **Table of Contents** - [Surveys](#surveys) - [RL-based agent](#rl-based-agent) - [Instruction following](#instruction-following) - [Build agent based on World model](#build-agent-based-on-world-model) - [Language as knowledge](#language-as-knowledge) - [LLM as a tool](#llm-as-a-tool) - [Generalization across tasks](#generalization-across-tasks) - [Continual learning](#continual-learning) - [Combine RL and LLM](#combine-rl-and-llm) - [Transformer-based policy](#transformer-based-policy) - [Trajectory to language](#trajectory-to-language) - [Trajectory predication](#trajectory-predication) - [Others](#others) - [LLM-based agent](#llm-based-agent) - [Multimodal](#multimodal) - [Train LLM for generalization & adaptation](#train-llm-for-generalization--adaptation) - [Task-specific designing](#task-specific-designing) - [Multi-agent (e.g., society, coperation)](#multi-agent-eg-society-coperation) - [Experimental analysis](#experimental-analysis) - [Benchmark & Dataset](#benchmark--dataset) - [Applications](#applications) - [Algorithm design](#algorithm-design) - [Combined with RL](#combined-with-rl) - [Others](#others-1) <!-- END doctoc generated TOC please keep comment here to allow auto update --> ------ ## Surveys - [A Survey on Large Language Model based Autonomous Agents](https://arxiv.org/pdf/2308.11432.pdf?trk=public_post_comment-text) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://arxiv.org/pdf/2309.07864.pdf) ## RL-based agent ### Instruction following - [NeurIPS'23] [Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation](https://arxiv.org/abs/2302.09368) - [NeurIPS'23] [Guide Your Agent with Adaptive Multimodal Rewards](https://openreview.net/attachment?id=G8nal7MpIQ&name=pdf) [[project](https://sites.google.com/view/2023arp)] - [Compositional
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Xixi Wu · The Chinese University of Hong Kong · Hong Kong
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Xiaojian Ma
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Zijian Zhou · National University of Singapore · Singapore
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Pei (Patrick) Chen
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a3f3596948ddee18, topic:large-language-models
matched fp:a3f3596948ddee18, topic:natural-language-processing
matched fp:a3f3596948ddee18, topic:reinforcement-learning
matched fp:a3f3596948ddee18, name:autonomous agent, desc:autonomous agent