jit-agent-learn 🧪 ALPHA
Part of Agent Forge Framework - Learning & Adaptation POC
Agent architecture with continuous learning and self-improvement capabilities
Overview
jit-agent-learn extends the JIT agent architecture with reinforcement learning capabilities, enabling agents to improve their performance through experience and feedback loops. This experimental system explores how AI agents can continuously evolve their decision-making processes.
🧠 Reinforcement Learning
Continuous improvement through experience-based learning mechanisms.
🔄 Adaptive Systems
Dynamic adjustment of behavior based on performance feedback.
📈 Self-Improvement
Autonomous enhancement of capabilities through iterative learning.
🎯 Feedback Loops
Sophisticated feedback mechanisms for performance optimization.
Learning Architecture
1. Experience Collection
Agent interactions and outcomes are captured and stored for analysis
2. Pattern Recognition
Machine learning algorithms identify successful strategies and failure patterns
3. Strategy Optimization
Behavioral parameters are adjusted based on learned patterns
4. Performance Enhancement
Improved strategies are deployed for better task execution