Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control (2408.17380v1)

Abstract: Model-based reinforcement learning (RL) is anticipated to exhibit higher sample efficiency compared to model-free RL by utilizing a virtual environment model. However, it is challenging to obtain sufficiently accurate representations of the environmental dynamics due to uncertainties in complex systems and environments. An inaccurate environment model may degrade the sample efficiency and performance of model-based RL. Furthermore, while model-based RL can improve sample efficiency, it often still requires substantial training time to learn from scratch, potentially limiting its advantages over model-free approaches. To address these challenges, this paper introduces a knowledge-informed model-based residual reinforcement learning framework aimed at enhancing learning efficiency by infusing established expert knowledge into the learning process and avoiding the issue of beginning from zero. Our approach integrates traffic expert knowledge into a virtual environment model, employing the Intelligent Driver Model (IDM) for basic dynamics and neural networks for residual dynamics, thus ensuring adaptability to complex scenarios. We propose a novel strategy that combines traditional control methods with residual RL, facilitating efficient learning and policy optimization without the need to learn from scratch. The proposed approach is applied to CAV trajectory control tasks for the dissipation of stop-and-go waves in mixed traffic flow. Experimental results demonstrate that our proposed approach enables the CAV agent to achieve superior performance in trajectory control compared to the baseline agents in terms of sample efficiency, traffic flow smoothness and traffic mobility. The source code and supplementary materials are available at https://github.com/zihaosheng/traffic-expertise-RL/.

• The paper introduces a knowledge-informed model-based residual reinforcement learning framework that integrates traffic domain knowledge to improve control of connected automated vehicles (CAVs).
• The methodology combines a hybrid Intelligent Driver Model (IDM) and neural network virtual environment with a residual RL agent that fine-tunes a physics-based initial control policy.
• Experimental results show the framework outperforms model-free and conventional model-based RL methods in learning efficiency and traffic smoothness, effectively reducing stop-and-go waves in various traffic scenarios.

Knowledge-Informed Reinforcement Learning for CAV Trajectory Control

The presented paper introduces a knowledge-informed model-based residual reinforcement learning (RL) framework, aimed at improving the control of connected automated vehicles (CAVs) in mixed traffic environments. The research leverages domain knowledge from traffic science to enhance RL approaches, effectively addressing the challenges inherent in traditional model-free and model-based RL methodologies.

The authors present a novel synthesis of the Intelligent Driver Model (IDM) with neural networks to form a robust, adaptable virtual environment model. This hybrid model combines well-established traffic dynamics with data-driven learning to address complex, real-world uncertainties. By integrating traffic expert knowledge, the approach ensures a reliable baseline understanding of vehicle behavior, while residual neural components capture the dynamics not encapsulated by IDM.

Methodological Advances

The key innovation presented is the integration of residual RL with traditional control strategies, forming a knowledge-informed model-based residual RL architecture. The research employs a physics-based initial policy, specifically the Proportional-Integral (PI) with saturation controller, acting as a stable and efficient starting point. The residual RL agent serves to fine-tune this policy, adapting to nuances in the traffic environment.

The framework comprises a virtual environment where IDM provides primary predictive dynamics, complemented by neural networks to estimate discrepancies and uncertainties (residuals). This setup allows for efficient, simulation-based learning before deploying in real-world conditions, significantly enhancing data efficiency. The authors provide rigorous theoretical proofs of convergence and performance bounds, substantiating their framework's efficacy over baseline model-free and conventional model-based methods.

Experimental Insights

The experiments conducted in various traffic configurations—a ring road, figure-eight road, and merge scenario typology—demonstrate the proposed framework's effectiveness. Notably, the system outperforms prominent RL algorithms such as Soft Actor-Critic (SAC), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO) concerning learning efficiency and traffic smoothness. The framework's ability to attain superior performance with fewer interactions, evidenced by higher reward attainments and less variance in velocity distributions, signifies its potential for real-world application in traffic management systems.

Figure-based analyses underscore the approach's success in reducing stop-and-go waves, often a principal challenge in traffic flow management, effectively enhancing overall traffic stability and efficiency. Through the application of a knowledge-driven virtual model, the paper provides significant insights into managing dynamic and complex CAV interactions, with substantial improvements in sample efficiency.

Implications and Future Directions

This paper contributes to the broader endeavor of integrating domain expertise into machine learning for advanced vehicular control systems. By capitalizing on well-understood physical models and the adaptability of neural networks, this framework remains versatile and open to further enhancements and hybridizations. While the current experiments provide a substantial evaluation of the framework's capabilities, future research could extend to sim-to-real transfer learning for actual traffic conditions, which remains a critical step for operational deployment.

The authors suggest further exploration into multi-agent reinforcement learning paradigms, which could exploit cooperative strategies among CAVs to expand upon collision avoidance mechanisms and improve network-wide traffic flows. The linkage of this research with meta-learning and transfer learning techniques offers promising avenues for rapid adaptability across varied traffic scenarios.

In summary, the paper adeptly addresses the pitfalls of traditional RL in vehicular control by embedding domain knowledge through a model-based residual learning strategy, paving the way for more efficient and scalable deployment of CAVs within intelligent transportation systems.