PBRL 2013 - International Workshop on Reinforcement Learning with Generalized Feedback: Beyond Numeric Rewards

In recent years, different generalizations of the standard setting of reinforcement learning have emerged; in particular, several attempts have been made to relax the quite restrictive requirement for numeric feedback and to learn from different types of more flexible training information. Examples of generalized settings of that kind include learning from expert demonstration (e.g., apprenticeship learning or inverse reinforcement learning), learning from qualitative feedback (e.g., ordinal MDPs or preference-based reinforcement learning), and learning from multiple feedback signals (e.g., multi-objective reinforcement learning). These extensions and variants of reinforcement learning are closely connected and largely intersecting with preference learning, a new subfield of machine learning that
deals with the learning of (predictive) preference models from observed/revealed or automatically extracted preference information. The goal of this workshop is to help in unifying and streamlining research on generalizations of standard reinforcement learning, which, for the time being, seem to be pursued independently. Ideally, the workshop will help the participants to identify some common ground of their work, thereby helping the field move toward a theoretical foundation of reinforcement learning with generalized feedback.