Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Better Generalization with Forecasts

Citations in the corpus

• The Predictron: End-To-End Learning and Planning (2016, background): “Such predictions have been shown to be useful as representations (Schaul & Ring, 2013) and for transfer (Schaul et al., 2015).”
• Learning by Playing - Solving Sparse Reward Tasks from Scratch (2018, background): “…connected to learning to predict the future via “successor” representations (Dayan, 1993; Kulka-rni et al., 2016b; Barreto et al., 2017) or forecasts (Schaul & Ring, 2013; Lample & Chaplot, 2017; Dosovitskiy & Koltun, 2017) and are trained to be predictive of features extracted from future states.”
• The Barbados 2018 List of Open Issues in Continual Learning (2018, unknown): “Auxiliary questions can be constructed bottom-up by identifying other signals, and predicting (or controlling) accumulations of those signals [6, 12].”
• Universal Value Function Approximators (2015, methodology): “Finally, a collection of value functions can be used as a predictive representation of state, where the predicted values themselves are used as a feature vector (Sutton & Tanner, 2005; Schaul & Ring, 2013).”