DMQ: Dual-Mode Q-Learning Hardware Accelerator for Shortest Path and Coverage Area 🔗Abstract: In this paper, we propose a novel dual-mode Q-learning hardware accelerator (DMQ) for shortest path and coverage area problems. The hardware accelerator design uses only an agent to tackle multiple modes, in this case, shortest path and coverage area problems for mobile robots. The work proposes a modified policy generator that supports two reward functions for the shortest path and coverage area.

MazeCov-Q: An Efficient Maze-Based Reinforcement Learning Accelerator for Coverage 🔗Abstract: Reinforcement learning (RL) is an unsupervised machine learning that does not requires pre-assigned labeled data to learn. It is implemented in many areas such as robotics, games, finances, health, transportation, and energy applications. In this paper, we present an application of reinforcement learning accelerator for finding coverage area and its implementation in a mobile robot called MazeCov-Q (Maze-Based Coverage Q-Learning). We define a novel state that is divided into two conditions.

Bayesian optimization is an optimization algorithm that uses Bayes Theorem to guide the sampling process to find the function minima/maxima. Bayesian optimization is best suited for optimization over continuous domain of less than 20 dimensions, and tolerates stochastic noise in the evaluation. It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian technique. After that the method uses an acquisition function defined from the surrogate function to decide which point to sample. One way to perform bayesian optimization is using scikit-optimize with python. Scikit-optimize can be used to optimize the function with single parameter or multiple parameter.