TTR-Based Reward for Reinforcement Learning with Implicit Model Priors
Xubo Lyu,Mo Chen,Xubo Lyu,Mo Chen
Model-free reinforcement learning (RL) is a powerful approach for learning control policies directly from high-dimensional state and observation. However, it tends to be data-inefficient, which is especially costly in robotic learning tasks. On the other hand, optimal control does not require data if the system model is known, but cannot scale to models with high-dimensional states and observation...
