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Deep reinforcement learning algorithms can learn complex behavioral skills,but real-world application of these methods requires a large amount ofexperience to be collected by the agent. In practical settings, such asrobotics, this involves repeatedly attempting a task, resetting the environmentbetween each attempt. However, not all tasks are easily or automaticallyreversible. In practice, this learning process requires extensive humanintervention. In this work, we propose an autonomous method for safe andefficient reinforcement learning that simultaneously learns a forward and resetpolicy, with the reset policy resetting the environment for a subsequentattempt. By learning a value function for the reset policy, we canautomatically determine when the forward policy is about to enter anon-reversible state, providing for uncertainty-aware safety aborts. Ourexperiments illustrate that proper use of the reset policy can greatly reducethe number of manual resets required to learn a task, can reduce the number ofunsafe actions that lead to non-reversible states, and can automatically inducea curriculum.

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning