Agents: Symbolic Learning Framework for Language Agents
Agents introduce an advanced framework tailored for training language agents, inspired by the learning techniques seen in neural networks. This method harmonizes traditional AI agent systems with neural network structures, revolutionizing the training of language-based AI agents for greater efficiency and effectiveness.
Key Features
- Analogous Structure to Neural Networks: The framework mirrors neural networks structure with concepts such as Agent Pipeline, Nodes, and Prompts acting as the building blocks for training language agents.
- Core Components:
- Loss Function: Utilizes prompt-based evaluation for effective assessment.
- Back-Propagation: Provides textual insights for each node for improved learning.
- Weight Optimizer: Adjusts symbolic components using language gradients.
- Training Process:
- Forward Pass: Executes actions and stores essential data for analysis.
- Loss Evaluation: Assesses outcomes using prompt-based metrics.
- Back-Propagation: Refines learning by generating language gradients.
Multi-Agent Support
Agents seamlessly expand to multi-agent structures by treating nodes as individual agents, enabling multiple agents to operate within a single node, fostering collaborative learning and interaction.
Use Cases and Demonstrations
The framework demonstrates its adaptability through various scenarios such as:
- NLP Classroom for interactive teaching environments
- Prisoner's Dilemma illustrating game theory concepts
- Software Design facilitating collaborative coding
- Database Administrator for anomaly detection
- Text Evaluation (ChatEval) using a multi-agent referee system
- Pokemon interactive game world with multiple characters (available in release-0.1)
Benefits
Agents offer:
- Enhanced training efficiency for language-based AI agents
- A structured approach to agent learning and optimization
- Support for both single-agent and multi-agent scenarios
- Enablement of complex, interactive simulations across various domains
Getting Started
To explore Agents' capabilities, users can engage with demo scenarios via the AgentVerse command-line interface, providing unique insights into the framework's applicability across different contexts. For developers and researchers, Agents present new horizons in AI agent training by merging symbolic AI with neural network learning, fostering advanced language-based AI systems adaptable to various challenges.
