Mixture Policy

Mixture policies in reinforcement learning combine multiple individual policies to improve performance, robustness, and sample efficiency. Current research focuses on developing effective methods for parameterizing and optimizing these mixtures, including approaches using Gaussian mixture models, ensemble methods, and risk-neutral/risk-averse policy combinations. These techniques are being applied across diverse domains, from robotics and finance to combinatorial optimization, demonstrating their potential to enhance the performance of reinforcement learning algorithms in complex and uncertain environments. The resulting improvements in sample efficiency and generalization capability are significant for both theoretical understanding and practical applications.