Agent Aria (Beta)

What is Agent Aria (Beta)?

Agent Aria (Beta) is an innovative tool designed to enhance the training of language agents through a method calledIntention-Driven Reward Aggregation. This approach helps language agents perform complex reasoning and decision-making tasks more effectively. By projecting natural language actions into a lower-dimensional intention space, Agent Aria reduces the complexity of the action space, making it easier to train agents in environments like negotiation or question-asking games.

Benefits

Agent Aria offers several key benefits:

• Reduced Reward Sparsity: By aggregating rewards in the intention space, Agent Aria addresses the issue of extreme reward sparsity, which can hinder effective reinforcement learning.
• Improved Policy Optimization: The tool significantly reduces gradient variance, leading to more stable and efficient policy updates.
• Performance Gains: Extensive experiments show that Agent Aria delivers substantial performance improvements, with an average gain of 9.95% across four downstream tasks, including negotiation and text-based games.
• Generalizability: The method is model-agnostic, meaning it can be applied to various large language models, making it versatile and widely applicable.

Use Cases

Agent Aria is particularly useful in environments where language agents need to perform complex reasoning and decision-making. Some key use cases include:

• Negotiation Games: Agent Aria helps agents strategize and negotiate effectively, considering factors like inflation and game dynamics.
• Question-Asking Games: The tool enables agents to ask strategic questions to identify target objects efficiently.
• Text-Based Games: Agent Aria improves the performance of agents in various text-based games, making them more effective and efficient.

Additional Information

Agent Aria is developed by researchers from Fudan University and Bytedance Seed. The method has been extensively tested and has shown significant improvements over strong offline and online reinforcement learning baselines. The tool is designed to be model-agnostic, making it applicable to a wide range of large language models.

For more detailed information, you can refer to the research paper titled 'ARIA: Training Language Agents with Intention-Driven Reward Aggregation' by Yang, Ruihan et al., available on arXiv.