The central assumption in machine learning that data are independent and identically distributed does not hold in many reinforcement learning settings, as experiences of reinforcement learning agents are sequential and intrinsically correlated in time. Berrueta and colleagues use the mathematical theory of ergodic processes to develop a reinforcement framework that can decorrelate agent experiences and is capable of learning in single-shot deployments.
- Thomas A. Berrueta
- Allison Pinosky
- Todd D. Murphey