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Discovering achievements with a hierarchical structure in procedurally generated environments presents a significant challenge.
Algorithms for the assignment and transportation problems
James Munkres · 1957
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Long short-term memory
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Efficient backprop
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Rectified linear units improve restricted boltzmann machines
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
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Iterative bregman projections for regularized transportation problems
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Adam: A method for stochastic optimization
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Mastering the game of go with deep neural networks and tree search
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Openai baselines
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Proximal policy optimization algorithms
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Distributional reinforcement learning with quantile regression
Will Dabney, Mark Rowland, Marc Bellemare, and Rémi Munos · 2018
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Representation learning with contrastive predictive coding
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Online and offline reinforcement learning by planning with a learned model
Julian Schrittwieser, Thomas K Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, and David Silver · 2021
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Data-efficient reinforcement learning with self-predictive representations
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Procedural generalization by planning with self-supervised world models
Ankesh Anand, Jacob C Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Theophane Weber, and Jessica B Hamrick · 2022
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Video pretraining (VPT): Learning to act by watching unlabeled online videos
Bowen Baker, Ilge Akkaya, Peter Zhokov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, and Jeff Clune · 2022
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Mastering atari, go, chess and shogi by planning with a learned model
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Cross-trajectory representation learning for zero-shot generalization in RL
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Rethinking value function learning for generalization in reinforcement learning
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Learning to generalize with object-centric agents in the open world survival game crafter
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Investigating the role of model-based learning in exploration and transfer
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Emergence of maps in the memories of blind navigation agents
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