Agent Q
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Agent Q
Overview :
Agent Q is the next-generation AI agent model developed by MultiOn. By integrating search, self-criticism, and reinforcement learning, it creates advanced autonomous web agents capable of planning and self-repair. It addresses the challenges of traditional large language models (LLMs) in multi-step reasoning tasks within dynamic environments, enhancing success rates in complex scenarios using guided Monte Carlo Tree Search (MCTS), AI self-criticism, and Direct Preference Optimization (DPO) algorithms.
Target Users :
Agent Q targets developers and consumers, especially those who need to perform multi-step reasoning and decision-making in dynamic and complex environments. For instance, it can be utilized for automated web navigation, data analysis, and executing complex tasks, thereby enhancing efficiency and accuracy.
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Use Cases
In reservation experiments conducted on Open Table, the success rate reached 95.4%.
Developers can utilize Agent Q for complex web data collection and analysis tasks.
Consumers can use Agent Q for automated online booking and inquiry services.
Features
Guided Search and MCTS: Autonomously generate data, explore different actions and web pages, balancing exploration and exploitation.
AI Self-Criticism: Provide feedback at each step, optimizing the decision-making process, crucial for long-term tasks.
Direct Preference Optimization (DPO): Fine-tune the model using preference pairs derived from MCTS-generated data.
Reinforcement Learning: Train the model using human feedback, enhancing its generalization ability for multi-step reasoning tasks.
Autonomous Data Collection: Significantly improved zero-shot performance of the LLaMa-3 model during reservation experiments conducted on Open Table.
Online Search Integration: Further enhanced the model's success rate in complex environments.
How to Use
1. Register and obtain access to Agent Q.
2. Set the task objectives and parameters of Agent Q based on your needs.
3. Launch Agent Q for autonomous data collection and task execution.
4. Monitor Agent Q's performance and make adjustments based on feedback.
5. Utilize the outputs from Agent Q for further analysis or decision-making.
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