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NeurIPS 2020 Papers

Conference on Neural Information Processing Systems

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1,901papers

Counted from the official paper list; may differ slightly from the number the conference announced.

  • Oral
    1065.6%
  • Spotlight
    28014.7%
  • Poster
    1,51379.6%
  • Other / unlabeled
    20.1%

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Topic: World Models · 2 papers · Reset filters

Latent World Models For Intrinsically Motivated Exploration

Aleksandr Ermolov, Nicu Sebe

Expand abstract

In this work we consider partially observable environments with sparse rewards. We present a self-supervised representation learning method for image-based observations, which arranges embeddings respecting temporal distance of observations. This representation is empirically robust to stochasticity and suitable for novelty detection from the error of a predictive forward model. We consider episodic and life-long uncertainties to guide the exploration. We propose to estimate the missing information about the environment with the world model, which operates in the learned latent space. As a motivation of the method, we analyse the exploration problem in a tabular Partially Observable Labyrinth. We demonstrate the method on image-based hard exploration environments from the Atari benchmark and report significant improvement with respect to prior work. The source code of the method and all the experiments is available at https://github.com/htdt/lwm.

Spotlight
World Models
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang

Expand abstract

Sample efficiency has been one of the major challenges for deep reinforcement learning. Recently, model-based reinforcement learning has been proposed to address this challenge by performing planning on imaginary trajectories with a learned world model. However, world model learning may suffer from overfitting to training trajectories, and thus model-based value estimation and policy search will be prone to be sucked in an inferior local policy. In this paper, we propose a novel model-based reinforcement learning algorithm, called BrIdging Reality and Dream (BIRD). It maximizes the mutual information between imaginary and real trajectories so that the policy improvement learned from imaginary trajectories can be easily generalized to real trajectories. We demonstrate that our approach improves sample efficiency of model-based planning, and achieves state-of-the-art performance on challenging visual control benchmarks.

Poster
World Models