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Found 3 papers, 1 papers with code
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
We formalize human intervention for RL and show how to reduce the human labor required by training a supervised learner to imitate the human's intervention decisions.
Learning the Preferences of Ignorant, Inconsistent Agents
If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning.
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference
Probabilistic programming languages allow modelers to specify a stochastic process using syntax that resembles modern programming languages.
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