  Autonomous driving holds promise for increased safety, optimized traffic
management, and a new level of convenience in transportation. While model-based
reinforcement learning approaches such as MuZero enables long-term planning,
the exponentially increase of the number of search nodes as the tree goes
deeper significantly effect the searching efficiency. To deal with this
problem, in this paper we proposed the expert-guided motion-encoding tree
search (EMTS) algorithm. EMTS extends the MuZero algorithm by representing
possible motions with a comprehensive motion primitives latent space and
incorporating expert policies toimprove the searching efficiency. The
comprehensive motion primitives latent space enables EMTS to sample arbitrary
trajectories instead of raw action to reduce the depth of the search tree. And
the incorporation of expert policies guided the search and training phases the
EMTS algorithm to enable early convergence. In the experiment section, the EMTS
algorithm is compared with other four algorithms in three challenging
scenarios. The experiment result verifies the effectiveness and the searching
efficiency of the proposed EMTS algorithm.
