VIREL: A Variational Inference Framework for Reinforcement Learning

NeurIPS 2019 Matthew FellowsAnuj MahajanTim G. J. RudnerShimon Whiteson

Applying probabilistic models to reinforcement learning (RL) enables the application of powerful optimisation tools such as variational inference to RL. However, existing inference frameworks and their algorithms pose significant challenges for learning optimal policies, e.g., the absence of mode capturing behaviour in pseudo-likelihood methods and difficulties learning deterministic policies in maximum entropy RL based approaches... (read more)

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