Hamid Eghbal-Zadeh, Florian Henkel, Gerhard Widmer,
"Context-Adaptive ReinforcementLearning using Unsupervised Learning of Context Variables"
: Proceedings of the pre-registration Workshop - NeurIPS 2020, 2020
Original Titel:
Context-Adaptive ReinforcementLearning using Unsupervised Learning of Context Variables
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the pre-registration Workshop - NeurIPS 2020
Original Kurzfassung:
In Reinforcement Learning (RL), changes in the context often cause a distributional
change in the observations of the environment, requiring the agent to adapt to this
change. For example, when a new user interacts with a system, the system has to
adapt to the needs of the user, which might differ based on the user?s characteristics
that are often not observable. In this Contextual Reinforcement Learning (CRL)
setting, the agent has to not only recognise and adapt to a context, but also remember
previous ones. However, often in CRL the context is unknown, hence a supervised
approach for learning to predict the context is not feasible. In this paper, we
introduce Context-Adaptive Reinforcement Learning Agent (CARLA), that is
capable of learning context variables in an unsupervised manner, and can adapt
the policy to the current context. We provide a hypothesis based on the generative
process that explains how the context variable relates to the states and observations
of an environment. Further, we propose an experimental protocol to test and
validate our hypothesis; and compare the performance of the proposed approach
with other methods in a CRL environment