Prediction problems inspired by animal learning
We present three problems modeled after animal learning experiments designed to test online state construction or representation learning algorithms. Our test problems require the learning system to construct compact summaries of their past interaction with the world in order to predict the future, updating online and incrementally on each time step without an explicit training-testing split...
The majority of recent work in Deep Reinforcement Learning focuses on either fully observable tasks, or games where stacking a handful of recent frames is sufficient for good performance. Current benchmarks used for evaluating memory and recurrent learning make use