Informing sequential clinical decision-making through reinforcement learning: an empirical study

Learning and Sequential Decision Making

Learning Decision Theoretic Utilities through Reinforcement Learning

Probability models can be used to predict outcomes and compensate for missing data, but even a perfect model cannot be used to make decisions unless the utility of the outcomes, or preferences between them, are also provided. This arises in many real-world problems, such as medical diagnosis, where the cost of the test as well as the expected improvement in the outcome must be considered. Relat...

Structure Learning in Human Sequential Decision-Making

Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. ...

Efficient Approximate Policy Iteration Methods for Sequential Decision Making in Reinforcement Learning

(Computer Science—Machine Learning) EFFICIENT APPROXIMATE POLICY ITERATION METHODS FOR SEQUENTIAL DECISION MAKING IN REINFORCEMENT LEARNING

Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making

Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine’s policy will prioritize each player’s interests o...