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Potential-based Reward Shaping in Sokoban
Zhao Yang, Mike Preuss, Aske Plaat
Learning to solve sparse-reward reinforcement learning problems is difficult, due to the lack of guidance towards the goal. But in some problems, prior knowledge can be used to aug…
High-Accuracy Model-Based Reinforcement Learning, a Survey
Aske Plaat, Walter Kosters, Mike Preuss
Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems from game playing and robotics have been solved w…
Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning
Matthias Müller-Brockhausen, Mike Preuss, Aske Plaat
The idea of transfer in reinforcement learning (TRL) is intriguing: being able to transfer knowledge from one problem to another problem without learning everything from scratch. T…