Publications

(2020). Conditional Importance Sampling for Off-Policy Learning. AISTATS.

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(2020). Adaptive Trade-Offs in Off-Policy Learning. AISTATS.

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(2020). Fast Task Inference with Variational Intrinsic Successor Features. ICLR.

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(2019). Hindsight Credit Assignment. NeurIPS.

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(2019). A Geometric Perspective on Optimal Representations for Reinforcement Learning. NeurIPS.

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(2019). Recurrent Experience Replay in Distributed Reinforcement Learning. ICLR.

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(2019). The Termination Critic. AISTATS.

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(2018). Implicit Quantile Networks for Distributional Reinforcement Learning. ICML.

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(2018). Autoregressive Quantile Networks for Generative Modeling. ICML.

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(2018). The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning. ICLR.

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(2018). Distributed Distributional Deterministic Policy Gradients. ICLR.

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(2018). An Analysis of Categorical Distributional Reinforcement Learning. AISTATS.

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(2018). Rainbow: Combining improvements in deep reinforcement learning. AAAI.

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(2018). Distributional Reinforcement Learning with Quantile Regression. AAAI.

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(2017). Successor features for transfer in reinforcement learning. NeurIPS.

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(2017). A distributional perspective on reinforcement learning. ICML.

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(2017). Statistics and Samples in Distributional Reinforcement Learning. ICML.

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