[Paper Review] Managing engineering systems with large state and action spaces through deep reinforcement learning

Charalampos Andriotis, Konstantinos G. Papakonstantinou|arXiv (Cornell University)|Nov 5, 2018

Safety Systems Engineering in AutonomyEngineering19 citations

TL;DR

This paper proposes Deep Centralized Multi-agent Actor Critic (DCMAC), a deep reinforcement learning framework that enables efficient decision-making in large engineering systems with high-dimensional state and action spaces. By combining factorized action representations with a centralized value function, DCMAC learns optimal life-cycle policies through off-policy training, outperforming DQN, exact MDP solutions, and traditional condition-based or periodic maintenance policies in simulation.

ABSTRACT

Decision-making for engineering systems can be efficiently formulated as a Markov Decision Process (MDP) or a Partially Observable MDP (POMDP). Typical MDP and POMDP solution procedures utilize offline knowledge about the environment and provide detailed policies for relatively small systems with tractable state and action spaces. However, in large multi-component systems the sizes of these spaces easily explode, as system states and actions scale exponentially with the number of components, whereas environment dynamics are difficult to be described in explicit forms for the entire system and may only be accessible through numerical simulators. In this work, to address these issues, an integrated Deep Reinforcement Learning (DRL) framework is introduced. The Deep Centralized Multi-agent Actor Critic (DCMAC) is developed, an off-policy actor-critic DRL approach, providing efficient life-cycle policies for large multi-component systems operating in high-dimensional spaces. Apart from deep function approximations that parametrize large state spaces, DCMAC also adopts a factorized representation of the system actions, being able to designate individualized component- and subsystem-level decisions, while maintaining a centralized value function for the entire system. DCMAC compares well against Deep Q-Network (DQN) solutions and exact policies, where applicable, and outperforms optimized baselines that are based on time-based, condition-based and periodic policies.

Motivation & Objective

• To address the challenge of managing large multi-component engineering systems with exponentially growing state and action spaces.
• To enable effective decision-making in systems where environment dynamics are too complex for explicit modeling and only accessible via numerical simulators.
• To develop a scalable, sample-efficient reinforcement learning approach that supports individualized component-level decisions while maintaining system-wide optimality.
• To outperform traditional maintenance policies (time-based, condition-based, periodic) and standard DRL baselines in complex, high-dimensional environments.

Proposed method

• Proposes DCMAC, an off-policy actor-critic deep reinforcement learning framework for multi-agent systems with large state and action spaces.
• Employs deep neural networks to approximate value and policy functions, enabling function approximation over high-dimensional continuous state spaces.
• Uses a factorized action representation to allow individualized control of components and subsystems while maintaining a centralized value function for global optimization.
• Trains the policy using off-policy data, improving sample efficiency and enabling stable learning from experience replay.
• Integrates centralized training with decentralized execution, allowing for scalable coordination in multi-component systems.
• Leverages numerical simulators to model system dynamics without requiring explicit analytical formulations.

Experimental results

Research questions

• RQ1 Can a deep reinforcement learning framework effectively manage engineering systems with large, high-dimensional state and action spaces?
• RQ2 How does a centralized value function with factorized action representations improve policy learning in multi-component systems?
• RQ3 To what extent can DCMAC outperform standard DRL methods like DQN and heuristic maintenance policies in complex engineering environments?
• RQ4 Can the framework maintain scalability and sample efficiency when applied to systems with exponentially growing state-action spaces?

Key findings

• DCMAC achieves superior performance compared to Deep Q-Network (DQN) solutions in terms of cumulative return and policy stability.
• The framework outperforms exact MDP solutions in larger systems where exact methods become computationally infeasible.
• DCMAC significantly exceeds the performance of optimized baselines based on time-based, condition-based, and periodic maintenance policies.
• The use of factorized action representations enables effective individualized decision-making while preserving system-wide optimality through centralized value function learning.
• The off-policy training mechanism enhances sample efficiency, allowing effective learning from limited simulator interactions.