Tag: Reinforcement Learning

More humanoid agents with Hierarchical Reinforcement Learning

More humanoid agents with Hierarchical Reinforcement Learning

As you may have guessed from its name, Hierarchical Reinforcement Learning (HRL) is a family of Reinforcement Learning algorithms that decompose the problem into different hierarchies of subproblems or subtasks, and the higher-qlevel tasks invoke the primitive lower-level tasks. The goal of HRL is to learn a multilayer policy to perform control at different levels…

Slotted ALOHA with Reinforcement Learning

In this article we will discuss the potential of reinforcement learning (RL) to learn a backoff control policy for slotted ALOHA-type random access. We will use deep reinforcement learning (DRL) to learn a policy for multi user random access system. Slotted ALOHA Background Slotted ALOHA (sALOHA) protocol [1] for random access in wireless networks has…

Autonomous Vehicle Control using Reinforcement Learning

In this article, we are going to explore an application of an autonomous driving vehicle using reinforcement learning. First, we introduce the basic theory behind the control system of an autonomous ground vehicle (AGV). Then we simulate some AGV examples using an open source library denominated Python Robotics. Finally, we present how we can optimize…