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Reinforcement learning has the potential to automate the acquisition ofbehavior in complex settings, but in order for it to be successfully deployed,a number of practical challenges must be addressed. First, in real worldsettings, when an agent attempts a task and fails, the environment must somehow"reset" so that the agent can attempt the task again. While easy in simulation,this could require considerable human effort in the real world, especially ifthe number of trials is very large. Second, real world learning often involvescomplex, temporally extended behavior that is often difficult to acquire withrandom exploration. While these two problems may at first appear unrelated, inthis work, we show how a single method can allow an agent to acquire skillswith minimal supervision while removing the need for resets. We do this byexploiting the insight that the need to "reset" an agent to a broad set ofinitial states for a learning task provides a natural setting to learn adiverse set of "reset-skills". We propose a general-sum game formulation thatbalances the objectives of resetting and learning skills, and demonstrate thatthis approach improves performance on reset-free tasks, and additionally showthat the skills we obtain can be used to significantly accelerate downstreamlearning.

Continual Learning of Control Primitives: Skill Discovery via Reset-Games