Online Learning Algorithms on Embedded Systems: An Investigation using the Matching Pennies Game

This project investigates the feasibility of deploying online learning algorithms on low-power, low-cost embedded systems. We explore this problem by implementing a mind-reading algorithm inspired by Shannon’s Mind Reading Paper and SEER (a sequence extracting robot) on a MicroBit embedded system to play the Matching Pennies game. We compare the performance of heuristic-based and reinforcement learning strategies under various memory and processor constraints. Additionally, we conduct a user study to validate our approach against human opponents. Our results demonstrate that online learning algorithms can be effectively deployed on embedded systems, providing new avenues for expansion, such as machine learning functionality through pruning and quantization, risk-aversion quantification, and federated learning approaches.