Factored temporal difference learning in the new ties environment - Gyenes Viktor; Bontovics Ákos; Lőrincz András; Symposium of Young Scientists on Intelligent Systems (2.) (2007) (Budapest) (2008)
Although reinforcement learning is a popular method for training an agent for decision making based on rewards, well studied tabular methods are not applicable for large, realistic problems. In this paper, we experiment with a factored version of temporal difference learning, which boils down to a linear function approximation scheme utilising natural features coming from the structure of the task. We conducted experiments in the New Ties environment, which is a novel platform for multi-agent simulations. We show that learning utilising a factored representation is effective even in large state spaces, furthermore it outperforms tabular methods even in smaller problems both in learning speed and stability, because of its generalisation capabilities.
Keywords: Természettudományok; Számítás- és információtudomány