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Recent breakthroughs in Go play and strategic games have witnessed the greatpotential of reinforcement learning in intelligently scheduling in uncertainenvironment, but some bottlenecks are also encountered when we generalize thisparadigm to universal complex tasks. Among them, the low efficiency of datautilization in model-free reinforcement algorithms is of great concern. Incontrast, the model-based reinforcement learning algorithms can revealunderlying dynamics in learning environments and seldom suffer the datautilization problem. To address the problem, a model-based reinforcementlearning algorithm with attention mechanism embedded is proposed as anextension of World Models in this paper. We learn the environment model throughMixture Density Network Recurrent Network(MDN-RNN) for agents to interact, withcombinations of variational auto-encoder(VAE) and attention incorporated instate value estimates during the process of learning policy. In this way, agentcan learn optimal policies through less interactions with actual environment,and final experiments demonstrate the effectiveness of our model in controlproblem.

VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control