Quantum-Enhanced Resilient Reinforcement Learning Using Causal Inference

Abstract

Deep Reinforcement Learning (DRL) is a sub-field of Machine Learning (ML) that combines reinforcement learning (RL) with deep learning techniques. DRL has showcased promising results in gaming environments and real-world applications. However, DRL agents are susceptible to faulty observations due to sudden interference like blackouts, frozen observation, or adversarial interference in practical applications. These scenarios can hamper the learning and performance of DRL agents if they are not resilient. Drawing inspiration from causal inference, the Causal In-ference Q-Network (CIQ) explores a Deep Q-Network (DQN) framework that undergoes training with an additional task focusing on training for observational interference. Through an evaluation conducted in various benchmark DQN environments, the experimental findings demonstrate that the CIQ method exhibits enhanced performance and greater resilience against observational interference. In this paper, quantum networks and hybrid quantum-classical networks are tested for their resilience in noisy environments. A novel Quantum-Enhanced CIQ-Network (QCIQ) is proposed which could replicate the functioning of the CIQ using Variational Quantum Circuits (VQCs) and is benchmarked against the corresponding classical networks for its performance.