System III: Learning with Domain Knowledge for Safety Constraints

Understand

Reinforcement learning agents naturally learn from extensive exploration.

• Exploration is costly and can be unsafe in $\textit{safety-critical}$ domains.
• This paper proposes a novel framework for incorporating domain knowledge to help guide safe exploration and boost sample efficiency.
• Previous approaches impose constraints, such as regularisation parameters in neural networks, that rely on large sample sets and often are not suitable for safety-critical domains where agents should almost always avoid unsafe actions.