Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones
Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones
Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration. We propose Recovery RL, an algorithm which navigates this tradeoff by (1) leveraging offline data to learn about constraint violating zones <italic …