  In the real world, the strong episode resetting mechanisms that are needed to
train agents in simulation are unavailable. The \textit{resetting} assumption
limits the potential of reinforcement learning in the real world, as providing
resets to an agent usually requires the creation of additional handcrafted
mechanisms or human interventions. Recent work aims to train agents
(\textit{forward}) with learned resets by constructing a second
(\textit{backward}) agent that returns the forward agent to the initial state.
We find that the termination and timing of the transitions between these two
agents are crucial for algorithm success. With this in mind, we create a new
algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which
intelligently switches between the two agents based on the agent's confidence
in achieving its current goal. Our new method achieves state-of-the-art
performance on several challenging environments for reset-free RL.
