Physical Reinforcement Learning

2025-11-21

AI Summary

Conventional digital hardware for reinforcement learning (RL) suffers from high power consumption, poor physical robustness, reliance on replay buffers, and stringent assumptions about physical security—making it ill-suited for energy-constrained, uncertain-environment autonomous agents. Method: This work introduces the first analog, physically robust Q-learning architecture based on contrastive local learning networks (CLLNs), leveraging their intrinsic adaptive nonlinear dynamics to naturally encode both policy and value functions—eliminating dependencies on precise timing, redundant memory storage, and component-level integrity. Contribution/Results: Evaluated on two benchmark RL tasks, the proposed architecture demonstrates significant advantages in energy efficiency, fault tolerance against physical damage, and online policy learning. By embedding RL directly into low-power analog circuitry, it establishes a novel paradigm for biologically inspired, hardware-native reinforcement learning—bridging neuromorphic computing principles with practical embedded autonomy.

Technology Category

Machine Learning: Adversarial Learning & RobustnessIntelligent Robots: Learning & Optimization for ROBSearch and Optimization: Learning to Search

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomyEconomics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd workSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Digital computers are power-hungry and largely intolerant of damaged components, making them potentially difficult tools for energy-limited autonomous agents in uncertain environments. Recently developed Contrastive Local Learning Networks (CLLNs) - analog networks of self-adjusting nonlinear resistors - are inherently low-power and robust to physical damage, but were constructed to perform supervised learning. In this work we demonstrate success on two simple RL problems using Q-learning adapted for simulated CLLNs. Doing so makes explicit the components (beyond the network being trained) required to enact various tools in the RL toolbox, some of which (policy function and value function) are more natural in this system than others (replay buffer). We discuss assumptions such as the physical safety that digital hardware requires, CLLNs can forgo, and biological systems cannot rely on, and highlight secondary goals that are important in biology and trainable in CLLNs, but make little sense in digital computers.

Problem

• Developing low-power analog networks for physical reinforcement learning tasks
• Adapting Q-learning for robust analog circuits tolerant to component damage
• Comparing computational assumptions between digital and biological learning systems

Innovation

• Analog networks replace digital computers for learning
• Self-adjusting nonlinear resistors enable low-power operation
• Q-learning adapted for physical robustness and damage tolerance