
Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning ...
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Malthusian Reinforcement Learning
Here we explore a new algorithmic framework for multiagent reinforcemen...
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ValueDecomposition Networks For Cooperative MultiAgent Learning
We study the problem of cooperative multiagent reinforcement learning w...
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Deep Reinforcement Learning in Large Discrete Action Spaces
Being able to reason in an environment with a large number of discrete a...
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Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with HighDimensional States and Actions
Many realworld problems come with action spaces represented as feature ...
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On Nicod's Condition, Rules of Induction and the Raven Paradox
Philosophers writing about the ravens paradox often note that Nicod's Co...
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Concentration and Confidence for Discrete Bayesian Sequence Predictors
Bayesian sequence prediction is a simple technique for predicting future...
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Optimistic Agents are Asymptotically Optimal
We use optimism to introduce generic asymptotically optimal reinforcemen...
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Principles of Solomonoff Induction and AIXI
We identify principles characterizing Solomonoff Induction by demands on...
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Feature Reinforcement Learning In Practice
Following a recent surge in using historybased methods for resolving pe...
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Peter Sunehag
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