Coarse-grained reconfigurable architecture (CGRA) has become a promising candidate for data-intensive computing due to its flexibility and high energy efficiency. CGRA compilers map data flow graphs (DFGs) extracted from applications onto CGRAs, playing a fundamental role in fully exploiting hardware resources for acceleration. Yet the existing compilers are time-demanding and cannot guarantee optimal results due to the traversal search of enormous search spaces brought about by the spatio-temporal flexibility of CGRA structures and the complexity of DFGs. Inspired by the amazing progress in reinforcement learning (RL) and Monte-Carlo tree search (MCTS) for real-world problems, we consider constructing a compiler that can learn from past experiences and comprehensively understand the target DFG and CGRA.