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A fundamental issue in reinforcement learning algorithms is the balancebetween exploration of the environment and exploitation of information alreadyobtained by the agent. Especially, exploration has played a critical role forboth efficiency and efficacy of the learning process. However, Existing worksfor exploration involve task-agnostic design, that is performing well in oneenvironment, but be ill-suited to another. To the purpose of learning aneffective and efficient exploration policy in an automated manner. Weformalized a feasible metric for measuring the utility of exploration based oncounterfactual ideology. Based on that, We proposed an end-to-end algorithm tolearn exploration policy by meta-learning. We demonstrate that our methodachieves good results compared to previous works in the high-dimensionalcontrol tasks in MuJoCo simulator.

Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy