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Autonomous driving holds promise for increased safety, optimized trafficmanagement, and a new level of convenience in transportation. While model-basedreinforcement learning approaches such as MuZero enables long-term planning,the exponentially increase of the number of search nodes as the tree goesdeeper significantly effect the searching efficiency. To deal with thisproblem, in this paper we proposed the expert-guided motion-encoding treesearch (EMTS) algorithm. EMTS extends the MuZero algorithm by representingpossible motions with a comprehensive motion primitives latent space andincorporating expert policies toimprove the searching efficiency. Thecomprehensive motion primitives latent space enables EMTS to sample arbitrarytrajectories instead of raw action to reduce the depth of the search tree. Andthe incorporation of expert policies guided the search and training phases theEMTS algorithm to enable early convergence. In the experiment section, the EMTSalgorithm is compared with other four algorithms in three challengingscenarios. The experiment result verifies the effectiveness and the searchingefficiency of the proposed EMTS algorithm.

Towards High Efficient Long-horizon Planning with Expert-guided Motion-encoding Tree Search