Demystifying the Educational Benefits of Different Gaming Genres

As research continues into the use of computer games for educational purposes, educators still appear reluctant to incorporate them into their teaching. One contributing factor to this reluctance is the lack of information regarding the benefits offered by the different games available today. These differences appear to have been largely overlooked by the academic community, resulting in a lack of information being made available to both the academic and education communities alike. Without this information, educators will find it difficult to determine whether a game will suit their teaching needs, and will continue to avoid using them. This paper studies a selection of games from several different genres, assessing each one in its ability to fulfil a set of previously identified requirements for a good educational resource. The results of the investigation showed that there were indeed strong differences between the genres, allowing for some suggestions to be made regarding their use in education, as well as leaving room for some interesting future work

DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning

We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explainability in the field of visual analytics