Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms
arXiv:2407.15283v2 Announce Type: replace Abstract: Industry is moving toward autonomous, network-connected machines that detect and adapt to changing conditions, including hardware faults. Conventional fault-tolerant design duplicates hardware and reroutes control logic; reinforcement learning (RL) offers a learning-based alternative. This paper presents the first systematic comparison of two RL algorithms -- Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) -- for integrating fault tolerance into control. Beyond algorithm choice, we investigate four knowledge-transfer strategies
The increasing complexity of autonomous systems and the need for greater resilience against hardware failures are driving research into novel fault tolerance mechanisms, moving beyond traditional redundancy.
This research highlights a significant advancement in autonomous system reliability by integrating machine learning for adaptive fault tolerance, crucial for critical infrastructure and advanced robotics.
The paradigm for designing fault-tolerant systems is shifting from purely hardware redundant approaches to incorporating learning-based, adaptive software solutions for resilience.
- · AI/ML developers
- · Robotics industry
- · Autonomous vehicle manufacturers
- · High-reliability computing sectors
- · Traditional hardware redundancy providers
- · Systems with static fault tolerance designs
Autonomous systems will become more robust and less susceptible to hardware failures during operation.
Reduced maintenance costs and increased operational uptime for complex machinery, potentially accelerating adoption in new sectors.
Greater societal trust and reliance on autonomous systems due to enhanced safety and reliability, paving the way for more pervasive integration.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG