Self-Evolving Multi-Agent Systems with Hierarchical Memory

Overview

This project develops a self-evolving multi-agent system where agents continuously improve their collaborative and individual capabilities through multi-round reinforcement learning interactions. Hierarchical memory structures enable efficient experience organization and strategy refinement over time.

Key Features

• Self-Evolving Agents: Autonomous strategy improvement through multi-round interactions
• Hierarchical Memory: Multi-level memory structures for organizing experiences at different abstraction levels
• Multi-Agent Collaboration: Coordinated learning and adaptation across agent teams
• Experience Replay: Structured replay mechanisms for efficient strategy refinement
• Emergent Behaviors: Self-organization and emergent coordination patterns

Research Directions

• Multi-round reinforcement learning for agent evolution
• Hierarchical memory architectures for multi-agent systems
• Self-improving agent strategies
• Emergent coordination and communication
• Evaluation frameworks for evolving multi-agent systems

Technologies

• Multi-Agent Reinforcement Learning
• Hierarchical Memory Architectures
• Self-Evolving Systems
• Deep RL
• PyTorch

Status

Ongoing research project. Submitted for peer review.