Continual Reinforcement Learning for Cyber-Physical Systems: Lessons Learned and Open Challenges

2025-11-19

AI Summary

This work addresses key challenges in continual reinforcement learning (CRL) for cyber-physical systems (e.g., autonomous driving): enabling agents to sequentially adapt to non-stationary, multi-task environments (e.g., successive parking maneuvers) while mitigating catastrophic forgetting, enhancing abstraction in environmental representation, and reducing sensitivity to hyperparameters and underutilization of neural capacity. We instantiate a sequential multi-scenario CRL framework based on Proximal Policy Optimization (PPO) and conduct empirical analysis in continuous control settings. Results expose structural limitations of mainstream RL architectures under CRL—particularly their inadequate adaptability to dynamic task evolution—challenging their suitability for real-world deployment. The study identifies several critical open problems and underscores the necessity of cross-disciplinary collaboration between computer science and neuroscience to co-design novel learning mechanisms and neuro-inspired representational architectures. These insights provide both theoretical reflection and an empirical benchmark for developing robust, scalable CRL systems.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Multiagent Systems: Multiagent Learning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Continual learning (CL) is a branch of machine learning that aims to enable agents to adapt and generalise previously learned abilities so that these can be reapplied to new tasks or environments. This is particularly useful in multi-task settings or in non-stationary environments, where the dynamics can change over time. This is particularly relevant in cyber-physical systems such as autonomous driving. However, despite recent advances in CL, successfully applying it to reinforcement learning (RL) is still an open problem. This paper highlights open challenges in continual RL (CRL) based on experiments in an autonomous driving environment. In this environment, the agent must learn to successfully park in four different scenarios corresponding to parking spaces oriented at varying angles. The agent is successively trained in these four scenarios one after another, representing a CL environment, using Proximal Policy Optimisation (PPO). These experiments exposed a number of open challenges in CRL: finding suitable abstractions of the environment, oversensitivity to hyperparameters, catastrophic forgetting, and efficient use of neural network capacity. Based on these identified challenges, we present open research questions that are important to be addressed for creating robust CRL systems. In addition, the identified challenges call into question the suitability of neural networks for CL. We also identify the need for interdisciplinary research, in particular between computer science and neuroscience.

Problem

• Addressing catastrophic forgetting in continual reinforcement learning systems
• Developing robust hyperparameter tuning for non-stationary cyber-physical environments
• Optimizing neural network capacity utilization across sequential learning tasks

Innovation

• Used Proximal Policy Optimization for training
• Experiments conducted in autonomous driving environment
• Identified challenges like catastrophic forgetting issues