Combating the Compounding-Error Problem with a Multi-step Model
arXiv: Learning, 2019.
Model-based reinforcement learning is an appealing framework for creating agents that learn, plan, and act in sequential environments. Model-based algorithms typically involve learning a transition model that takes a state and an action and outputs the next state---a one-step model. This model can be composed with itself to enable predi...More
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