Model-based Reinforcement Learning for Parameterized Action Spaces: Achieving Superior Sample Efficiency and Asymptotic Performance
We propose a novel model-based reinforcement learning algorithm, Dynamics Learning and predictive control with Parameterized Actions (DLPA), that achieves superior sample efficiency and asymptotic performance compared to state-of-the-art PAMDP methods.