Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learning

Adrian Remonda, Eduardo Veas, Granit Luzhnica

Model-based reinforcement learning (MBRL) aims to learn model(s) of the environment dynamics that can predict the outcome of its actions. Forward application of the model yields so…