Td MPC

Temporal Difference Model Predictive Control (TD-MPC) is a model-based reinforcement learning approach that optimizes control policies by combining learned dynamics models with short-horizon trajectory optimization. Current research focuses on improving the efficiency and robustness of TD-MPC, particularly through advancements in implicit world models, the integration of control barrier functions for safety, and the use of neural networks for handling uncertainty and complex dynamics, such as those found in robotics and autonomous driving. These improvements aim to enhance the scalability and real-time performance of TD-MPC, leading to wider applicability in diverse domains requiring precise and safe control, including robotic manipulation, autonomous vehicle navigation, and process optimization.