REAL Reinforcement Learning: Planning with adversarial models

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...

Q-value reuse between state abstractions for traffic light control

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...