Safe Reinforcement Learning Using Advantage-Based Intervention

Understand

Many sequential decision problems involve finding a policy that maximizes total reward while obeying safety constraints.

• Although much recent research has focused on the development of safe reinforcement learning (RL) algorithms that produce a safe policy after training, ensuring safety during training as well remains an open problem.
• A fundamental challenge is performing exploration while still satisfying constraints in an unknown Markov decision process (MDP).
• In this work, we address this problem for the chance-constrained setting.