Department of Computer Science: MSc Thesis Presentation
Continual Reinforcement Learning in a Resource Allocation Simulator
Author: Antti Kumpumäki
Supervisor: Pekka Marttinen
Date: Thursday 10 June 2021
Zoom: https://aalto.zoom.us/j/63809756445 (passcode: 363706)
Traditional deep reinforcement learning algorithms have suffered from slow learning of new concepts and catastrophic forgetting, where previously learned information is lost when new information is presented.
The aim of this thesis is to explore how deep reinforcement learning algorithms can be modified to make them more resilient to changes in the environment, and also to evaluate the usability of such algorithms in a resource allocation problem. These topics are approached by developing a simulator that mimics the internet usage of a population in an imaginary city, where the movement and behaviour of the population change suddenly. The network demand of different parts of this city are predicted using a Soft Actor-Critic algorithm, that is implemented with an experience replay buffer that can favour experiences from different time scales.