Robust and Scalable Autonomous Reinforcement Learning in Irreversible Environments

Reinforcement learning (RL) typically assumes repetitive resets to provide an agent with diverse and unbiased experiences. These resets require significant human intervention and result in poor training efficiency in real-world settings.