An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors

Abstract

Multi-agent reinforcement learning (RL) often struggles to ensure the safe behaviours of the learning agents, and therefore it is generally not adapted to safety-critical applications. To address this issue, we present a methodology that combines formal verification with (deep) RL algorithms to guarantee the satisfaction of formally-specified safety constraints both in training and testing. The approach we propose expresses the constraints to verify in Probabilistic Computation Tree Logic (PCTL) and builds an abstract representation of the system to reduce the complexity of the verification step. This abstract model allows for model checking techniques to identify a set of abstract policies that meet the safety constraints expressed in PCTL. Then, the agents' behaviours are restricted according to these safe abstract policies. We provide formal guarantees that by using this method, the actions of the agents always meet the safety constraints, and provide a procedure to generate an abstract model automatically. We empirically evaluate and show the effectiveness of our method in a multi-agent environment.

Efficient LTL Model Checking of Deep Reinforcement Learning Systems using Policy Extraction

“…Three features make it a challenging problem to verifying DRL systems. First, the state space of such control systems is usually infinite and continuous, but most of the model checking-based approaches can only handle finitestate models [20]. Second, the system dynamics are generally nonlinear, which increases the complexity of formal verification [4].…”