The fact that China’s authoritarian system is particularly poor at dealing with public health emergencies that require timely, transparent and accurate information makes this far more significant than any other challenge Mr Xi has faced so far.

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Deep reinforcement learning has achieved great successes inrecent years, however, one main challenge is the sample in-efficiency. In this paper, we focus on how to use action guid-ance by means of a non-expert demonstrator to improve sam-ple efficiency in a domain with sparse, delayed, and pos-sibly deceptive rewards: the recently-proposed multi-agentbenchmark of Pommerman. We propose a new frameworkwhere even a non-expert simulated demonstrator, e.g., plan-ning algorithms such as Monte Carlo tree search with a smallnumber rollouts, can be integrated within asynchronous dis-tributed deep reinforcement learning methods. Compared to avanilla deep RL algorithm, our proposed methods both learnfaster and converge to better policies on a two-player miniversion of the Pommerman game.