DMQL: Deep Maximum Q-Learning: Combatting Relative Overgeneralisation in Deep Independent Learners using Optimism and Similarity

Various pathologies can occur when independent learners are used in cooperative Multi-Agent Reinforcement Learning. One such pathology is Relative Overgeneralisation, which manifests when a suboptimal Nash Equilibrium in the joint action space of a problem is preferred over an optimal Equilibrium. Approaches exist to combat relative...

The Feasibility of Reinforcement Learning as a replacement for Mixed-Integer Programming for Smart Charging of Electric Vehicles

As the world is currently actively trying to reduce the consumption of fossil fuels, large investments are done in renewable energy sources and ways are sought after to electrify fossil fuel-intensive sectors. In line with these developments, the number of electric vehicles requiring access to the electric power grid has exploded putting...

Optimizing Edge Computing in 5G Networks

Multi-access Edge Computing (MEC) is a concept brought up by ETSI and it places computing, storage, processing and network resources into MEC hosts and places these MEC hosts as close as needed to the telecom network edge in order to reduce service latency and bandwidth usage. For self-driving vehicles, streaming video and real-time gaming, the...

Intelligent signalized intersection management for mixed traffic using Deep Q-Learning

Signalized urban intersections are bottlenecks for traffic and cause congestion. To improve traffic signal plans, research efforts have been made to create self-adaptive traffic controllers, i.e. controllers which adapt in real-time to the current traffic demand based on connected vehicle data. Past research on self-adaptive controllers has...

Fleet Planning Under Demand Uncertainty: A Reinforcement Learning Approach

This work proposes a model-free reinforcement learning approach to learn a long-term fleet planning problem subjected to air-travel demand uncertainty. The aim is to develop a dynamic fleet policy which adapts over time by intermediate assessments of the states. A Deep Q-network is trained to estimate the optimal fleet decisions based on the...

Multi Agent Deep Recurrent Q-Learning for Different Traffic Demands

In today’s scenario due to rapid urbanisation there has been a shift of population from rural to urban areas especially in developing countries in search of better opportunities. This has lead to unprecedented growth of cities leading to various urbanisation problems. One of the main problems that comes across in urban areas is the increased...

Limit order placement optimization with Deep Reinforcement Learning: Learning from patterns in cryptocurrency market data

For various reasons, financial institutions often make use of high-level trading strategies when buying and selling assets. Many individuals, irrespective or their level of prior trading knowledge, have recently entered the field of trading due to the increasing popularity of cryptocurrencies, which offer a low entry barrier for trading....