Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms

TL;DR

This work tackles hardware fault tolerance by applying reinforcement learning to continual adaptation, evaluating Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) in OpenAI Gym environments Ant-v2 and FetchReach-v1 under six faults. It investigates four knowledge-transfer strategies to carry forward human-curated normal-environment experience into fault settings, measuring adaptation speed, sample efficiency, and real-time performance. The study finds that transferring and fine-tuning normal-environment model parameters generally accelerates adaptation for PPO, while discarding prior knowledge often yields superior initial performance for SAC in certain faults; both algorithms demonstrate practical, minutes-scale adaptation to faults and can outperform some prior meta-learning approaches. These results highlight the potential for robust, adaptive machines able to operate under hardware faults with minimal downtime, informing design choices for real-world fault-tolerant robotic systems, and they open avenues for safer, selective knowledge transfer in dynamic, safety-critical settings where faults may arise unpredictably.

Abstract

Industry is rapidly moving towards fully autonomous and interconnected systems that can detect and adapt to changing conditions, including machine hardware faults. Traditional methods for adding hardware fault tolerance to machines involve duplicating components and algorithmically reconfiguring a machine's processes when a fault occurs. However, the growing interest in reinforcement learning-based robotic control offers a new perspective on achieving hardware fault tolerance. However, limited research has explored the potential of these approaches for hardware fault tolerance in machines. This paper investigates the potential of two state-of-the-art reinforcement learning algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), to enhance hardware fault tolerance into machines. We assess the performance of these algorithms in two OpenAI Gym simulated environments, Ant-v2 and FetchReach-v1. Robot models in these environments are subjected to six simulated hardware faults. Additionally, we conduct an ablation study to determine the optimal method for transferring an agent's knowledge, acquired through learning in a normal (pre-fault) environment, to a (post-)fault environment in a continual learning setting. Our results demonstrate that reinforcement learning-based approaches can enhance hardware fault tolerance in simulated machines, with adaptation occurring within minutes. Specifically, PPO exhibits the fastest adaptation when retaining the knowledge within its models, while SAC performs best when discarding all acquired knowledge. Overall, this study highlights the potential of reinforcement learning-based approaches, such as PPO and SAC, for hardware fault tolerance in machines. These findings pave the way for the development of robust and adaptive machines capable of effectively operating in real-world scenarios.

Figures (8)

• Figure 1: Overview of our study. A machine (robot) encounters a fault at $t{=}t^*$. We explore approaches for transferring knowledge acquired by the agent learning in a normal environment up to $t{=}t^*$ (${\mathcal{K}}_{t{=}t^*}$), thereby constructing a prior for the agent in a fault environment.
• Figure 2: The four distinct faults introduced to separate instances of the Ant-v2 environment. Links and/or joints affected by a fault are indicated in red.
• Figure 3: FetchReach-v1 faults. Links and/or joints affected by a fault are indicated in red.
• Figure 4: The state-visitation probability distribution of each joint in the Ant-v2 Ankle ROM Restriction fault environment is examined under two scenarios: (a) immediately after transferring all knowledge acquired in the normal environment --- the model parameters and the memory () or replay buffer () --- with no adaptation within the fault environment, and (b) after transferring all knowledge acquired in the normal environment, allowing for a period of adaptation (fine-tuning) within the fault environment. We observe a shift in the state-visitation distribution due to policy adaptation.
• Figure 5: Learning curves depicting the performance of and in four Ant-v2 fault environments, with the four knowledge transfer approaches. The average return is plotted against the number of training steps in millions, illustrating the adaptation and learning efficiency of each approach.