Inverse Reinforcement Learning Based Approach for Investigating Optimal Dynamic Treatment Regime

Variable selection for dynamic treatment regimes: a reinforcement learning approach

Dynamic treatment regimes (DTRs) can be inferred from data collected through some randomized clinical trials by using reinforcement learning algorithms. During these clinical trials, a large set of clinical indicators are usually monitored. However, it is often more convenient for clinicians to have DTRs which are only defined on a small set of indicators rather than on the original full set. T...

Score-based Inverse Reinforcement Learning

This paper reports theoretical and empirical results obtained for the score-based Inverse Reinforcement Learning (IRL) algorithm. It relies on a non-standard setting for IRL consisting of learning a reward from a set of globally scored trajectories. This allows using any type of policy (optimal or not) to generate trajectories without prior knowledge during data collection. This way, any existi...

Preference-learning based Inverse Reinforcement Learning for Dialog Control

Dialog systems that realize dialog control with reinforcement learning have recently been proposed. However, reinforcement learning has an open problem that it requires a reward function that is difficult to set appropriately. To set the appropriate reward function automatically, we propose preference-learning based inverse reinforcement learning (PIRL) that estimates a reward function from dia...

Inverse Reinforcement Learning for Marketing

Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of Stochastic Optimal Control. We suggest an alternative approach to study dynamic consumer demand, based on Inverse Reinforcement Learning (IRL). We ...

Algorithms for Inverse Reinforcement Learning