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Benefits and Shortcomings of Assistance

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Assistance games (also known as cooperative inverse RL) enable a single RL policy to both infer human preferences and act such that they are optimized. The idea is to model the human as a part of the environment, and the true reward function as a latent variable in the environment that the agent may make inferences about. Our talk will introduce the assistance paradigm, compare it to reward learning, and discuss its flaws in the context of AI Alignment.

This talk is part of the Machine Learning Reading Group @ CUED series.

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