Formal Language Constraints for Markov Decision Processes
This work addresses safety-critical constraints for reinforcement learning agents, offering a method to integrate formal verification techniques, though it is incremental as it builds on existing constrained MDP frameworks.
The paper tackles the problem of ensuring safety in reinforcement learning by structuring constraints using formal languages, specifically finite automata, to efficiently detect violations and learn joint dynamics. The result is improved training efficiency and safety across multiple RL algorithms in Safety Gym, MuJoCo, and Atari environments, with empirical evaluations showing concrete performance gains.
In order to satisfy safety conditions, an agent may be constrained from acting freely. A safe controller can be designed a priori if an environment is well understood, but not when learning is employed. In particular, reinforcement learned (RL) controllers require exploration, which can be hazardous in safety critical situations. We study the benefits of giving structure to the constraints of a constrained Markov decision process by specifying them in formal languages as a step towards using safety methods from software engineering and controller synthesis. We instantiate these constraints as finite automata to efficiently recognise constraint violations. Constraint states are then used to augment the underlying MDP state and to learn a dense cost function, easing the problem of quickly learning joint MDP/constraint dynamics. We empirically evaluate the effect of these methods on training a variety of RL algorithms over several constraints specified in Safety Gym, MuJoCo, and Atari environments.