GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

Multistep Electricity Price Forecasting for Deregulated Energy Markets: GAN-Based Reinforcement Learning

Electricity Price Forecasting (EPF) plays a vital role in smart grid applications for deregulated electricity markets. Most of the studies tend to investigate the electricity market influencers using forecasting techniques, often losing sight of significance on the sensibility of EPF models to the unstable real-time environment. This project will address a novel EPF based on deep reinforcement learning. The proposed approach uses generative adversarial networks (GAN) to collect synthetic data and increase training set effectively and increase the adaptation of the forecasting system to the environment. The data collected will be fed to a Deep Q learning to generate the final predictions. The proposed GAN-DQL will also be assessed on real data to prove the proposed model advantages compared to several machine learning solutions

Generating Levels and Playing Super Mario Bros. with Deep Reinforcement Learning Using various techniques for level generation and Deep Q-Networks for playing

Master's thesis in Information- and communication technology (IKT590)This thesis aims to explore the behavior of two competing reinforcement learning agents in Super Mario Bros. In video games, PCG can be used to assist human game designers by generating a particular aspect of the game. A human game designer can use generated game content as inspiration to build further upon, which saves time and resources. Much research has been conducted on AI in video games, including AI for playing Super Mario Bros. Additionally, there exists a research field focused on PCG for video games, which includes generation of Super Mario Bros. levels. In this thesis, the two fields of research are combined to form a GAN-inspired system of two competing AI agents. One agent is controlling Mario, and this agent represents the discriminator. The other agent generates the level Mario is playing, and represents the generator. In an ordinary GAN system, the generator is attempting to mimic a database containing real data, while the discriminator attempts to distinguish real data samples from the generated data samples. The Mario agent utilizes a DQN algorithm for learning to navigate levels, while the level generator uses a DQN-based algorithm with different types of neural networks. The DQN algorithm utilizes neural networks to predict the expected future reward for each possible action. The expected future rewards are denoted as Q-values. The results show that the generator is capable of generating content better than random when the generator model takes a sequence of tiles as input and produces a sequence of predictions of Q-values as output