Finite State Controller

Finite-state controllers (FSCs) are computational models that manage complex systems by transitioning between a finite number of states based on input conditions. Current research emphasizes using FSCs in reinforcement learning, particularly for safe and efficient control of nonlinear systems like soft robots and autonomous vehicles, often incorporating memory-based architectures such as LSTMs or finite memory controllers to improve performance and reduce training time. This focus stems from the need for verifiable safety guarantees and efficient control strategies in increasingly complex applications, with ongoing work exploring the trade-offs between controller complexity, performance, and the ability to formally verify safety properties.