Reinforcement learning and convolutional neural network system for firefighting rescue robot

Abstract

Abstract. In this paper, we combine the machine learning and neural network to build some modules for the fire rescue robot application. In our research, we build the robot legs module with Q-learning. We also finish the face detection with color sensors and infrared sensors. It is usual that image fusion is done when we want to use two kinds of sensors. Kalman filter is chosen to meet our requirement. After we finish some indispensable steps, we use sliding windows to choose our region of interest to make the system's calculation lower. The least step is convolutional neural network. We design a seven layers neural network to find the face feature and distinguish it or not.

Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents

“…The application of reinforcement learning (RL) models has been on a rise following the onset of deep reinforcement learning [1]. The ability of RL models to solve complex problems, represented mostly by the association of high-dimensional states and a large number of discrete or continuous actions, has recently led to the development of expert systems for guiding autonomous cars [2], [3], predicting the stock exchange impact [4], [5], and coordinating a swarm of robots to protect the environment [6], [7], to name a few examples.…”