Multi-agent reinforcement learning (MARL) has long been a significant research topic in
both machine learning and control systems. Recent development of (single-agent) deep …

Multi-agent deep reinforcement learning (MADRL) has been widely used in many scenarios
such as robotics and game AI. However, existing methods mainly focus on the optimization …

Model-based Reinforcement Learning (MBRL) allows data-efficient learning which is
required in real world applications such as robotics. However, despite the impressive data …

Reinforcement learning (RL) provides an appealing formalism for learning control policies
from experience. However, the classic active formulation of RL necessitates a lengthy active …

This book starts with an introduction to state-based reinforcement learning algorithms
involving Markov models, Bellman equations, and writing custom C# code with the aim of …

When compared with their single-agent counterpart, multi-agent systems have an additional
set of challenges for reinforcement learning algorithms, including increased complexity, non …

Heterogeneous Network (HetNet), where Small cell Base Stations (SBSs) are densely
deployed to offload traffic from macro Base Stations (BSs), is identified as a key solution to …

Advances in reinforcement learning research have recently produced agents that are
competent, or sometimes exceed human performance, in complex tasks. Most interesting …

Master different reinforcement learning techniques and their practical implementation using
OpenAI Gym, Python and JavaAbout This Book Take your machine learning skills to the next …

Breakthrough advances in reinforcement learning (RL) research have led to a surge in the
development and application of RL. To support the field and its rapid growth, several …