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Toward Human-AI Alignment in Large-Scale Multi-Player Games
First, we analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task space.
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning
We explore the benefits of augmenting state-of-the-art model-free deep reinforcement algorithms with simple object representations.
Scaling characteristics of sequential multitask learning: Networks naturally learn to learn
We explore the behavior of a standard convolutional neural net in a setting that introduces classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks.
Sequential mastery of multiple visual tasks: Networks naturally learn to learn and forget to forget
Through simulations involving sequences of ten related visual tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming multi-skill domain experts.
