Zero-shot Model-based Reinforcement Learning using Large Language Models
Smart state space math lets LLMs master robot control without special training.
Smart state space math lets LLMs master robot control without special training.
Mathematical transformation tricks enable LLMs to predict physical movements naturally
Original Problem 🎯:
Current reinforcement learning systems struggle with continuous state spaces and need extensive training data. While LLMs show promise in text-based tasks, their application to continuous control problems remains unexplored. The key challenge is handling multivariate data and incorporating control signals effectively.
Solution in this Paper 🛠️:
• Introduced Disentangled In-Context Learning (DICL) framework
• Uses Principal Component Analysis (PCA) to transform state-action data into uncorrelated space
• Applies in-context learning on transformed data to predict dynamics
• Implements two variants: DICL-(s) for state-only prediction and DICL-(s,a) for state-action prediction
Key Insights from this Paper 💡:
• LLMs can effectively predict continuous Markov decision process dynamics without fine-tuning
• PCA transformation significantly improves handling of multivariate data
• Well-calibrated uncertainty estimates from LLM predictions enhance safety
• Reduced dimensionality through PCA improves computational efficiency
• Integration with off-policy algorithms boosts sample efficiency
Results 📊:
• Improved sample efficiency in HalfCheetah and Hopper environments
• DICL-SAC shows 10-15% better early-stage learning compared to standard SAC
• Achieved 50% reduction in computational time through dimensionality reduction
• Demonstrated well-calibrated uncertainty with Kolmogorov-Smirnov statistic of 0.04-0.12
• Successfully validated on proprioceptive control tasks