AGENT FORGE: A Flexible Low-Code Platform for Reinforcement Learning Agent Design
AgentForge simplifies RL parameter optimization through a flexible low-code platform.
AgentForge simplifies RL parameter optimization through a flexible low-code platform.
Finally, a way to tune RL agents without pulling your hair out!
🎯 Original Problem:
Developing Reinforcement Learning (RL) agents requires optimizing numerous interrelated parameters across policy, reward function, environment, and internal architecture. This optimization is especially challenging for non-ML experts, making it difficult to leverage RL in fields like cognitive science.
🛠️ Solution in this Paper:
• AgentForge: A low-code platform for optimizing RL parameters
• Users only need to provide:
Configuration file for parameters
Custom environment
Evaluation method
• Supports multiple optimization algorithms:
Random Search
Bayesian Optimization
Particle Swarm Optimization
• Enables parallel evaluation of trials
• Allows joint or individual optimization of parameter sets
💡 Key Insights:
• Parameter optimization alone can significantly improve RL agent performance
• Joint optimization with Neural Architecture Search performs similarly to optimization without it
• Parallel evaluation is crucial - 8 parallel trials reduce optimization time from 50 days to 7 days
• Low-code approach makes RL accessible to non-ML experts
📊 Results:
• Bayesian Optimization achieved highest mean reward: 172.43
• PSO showed fastest convergence but lower reward: 119.19
• Random Search baseline: 84.26
• Evaluation of single candidate solution: ~3 hours on RTX 4090