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 achieve goals, acquire knowledge and continually improveme.
Specifically, this repo is interested in two types of agent: RL-based agent and LLM-based agent.
Table of Contents
- Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation
- Compositional Instruction Following with Language Models and Reinforcement Learning
- Learning to Model the World with Language
- MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
- Learning with Language Inference and Tips for Continual Reinforcement Learning
- Informing Reinforcement Learning Agents by Grounding Natural Language to Markov Decision Processes
- Language Reward Modulation for Pretraining Reinforcement Learning
- Leveraging Large Language Models for Optimised Coordination in Textual Multi-Agent Reinforcement Learning
- Text2Reward: Dense Reward Generation with Language Models for Reinforcement Learning
- Language to Rewards for Robotic Skill Synthesis
- Eureka: Human-Level Reward Design via Coding Large Language Models
- STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models
- ADAPTER-RL: Adaptation of Any Agent using Reinforcement Learning
- Online Continual Learning for Interactive Instruction Following Agents
- RoboGPT : An intelligent agent of making embodied long-term decisions for daily instruction tasks
- Can Language Agents Approach the Performance of RL? An Empirical Study On OpenAI Gym
- RLAdapter: Bridging Large Language Models to Reinforcement Learning in Open Worlds
- Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain
- A Competition Winning Deep Reinforcement Learning Agent in microRTS
- Aligning Agents like Large Language Models
- Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds
- Multimodal Web Navigation with Instruction-Finetuned Foundation Models
- You Only Look at Screens: Multimodal Chain-of-Action Agents
- Learning Embodied Vision-Language Programming From Instruction, Exploration, and Environmental Feedback
- An Embodied Generalist Agent in 3D World
- FireAct: Toward Language Agent Finetuning
- Adapting LLM Agents Through Communication
- AgentTuning: Enabling Generalized Agent Abilities for LLMs
- Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
- Rethinking the Buyer’s Inspection Paradox in Information Markets with Language Agents
- A Language-Agent Approach to Formal Theorem-Proving
- Agent Instructs Large Language Models to be General Zero-Shot Reasoners
- Ghost in the Minecraft: Hierarchical Agents for Minecraft via Large Language Models with Text-based Knowledge and Memory
- PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
- Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale
- Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4
- Building Cooperative Embodied Agents Modularly with Large Language Models
- OKR-Agent: An Object and Key Results Driven Agent System with Hierarchical Self-Collaboration and Self-Evaluation
- MetaGPT: Meta Programming for Multi-Agent Collaborative Framework
- AutoAgents: A Framework for Automatic Agent Generation
- Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization
- AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
- Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View
- REX: Rapid Exploration and eXploitation for AI agents
- Identifying the Risks of LM Agents with an LM-Emulated Sandbox
- Evaluating Multi-Agent Coordination Abilities in Large Language Models
- Large Language Models as Gaming Agents
- Benchmarking Large Language Models as AI Research Agents
- Adaptive Environmental Modeling for Task-Oriented Language Agents
- CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
- SmartPlay : A Benchmark for LLMs as Intelligent Agents
- AgentBench: Evaluating LLMs as Agents
- Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena
- SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
- SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series
- WebArena: A Realistic Web Environment for Building Autonomous Agents
- LLM-Deliberation: Evaluating LLMs with Interactive Multi-Agent Negotiation Game
- Evaluating Large Language Models at Evaluating Instruction Following
- CivRealm: A Learning and Reasoning Odyssey for Decision-Making Agents
- Lyfe Agents: generative agents for low-cost real-time social interactions
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
- Prospector: Improving LLM Agents with Self-Asking and Trajectory Ranking
- Formally Specifying the High-Level Behavior of LLM-Based Agents
- Cumulative Reasoning With Large Language Models