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Foffano, Daniele (author)
Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usually formalised as Markov Decision Processes, using a model of the environment dynamics to compute the optimal policy. When dealing with complex environments, the environment dynamics are frequently approximated with function approximators (such as...
master thesis 2022
Kuhn, Emanuel (author)
Previous research has in reinforcement learning for traffic control has used various state abstractions. Some use feature vectors while others use matrices of car positions. This paper first compares a simple feature vector consisting of only queue sizes per incoming lane to a matrix of car positions. Then it investigates if knowledge can be...
bachelor thesis 2020