  Reinforcement learning (RL) agents aim at learning by interacting with an
environment, and are not designed for representing or reasoning with
declarative knowledge. Knowledge representation and reasoning (KRR) paradigms
are strong in declarative KRR tasks, but are ill-equipped to learn from such
experiences. In this work, we integrate logical-probabilistic KRR with
model-based RL, enabling agents to simultaneously reason with declarative
knowledge and learn from interaction experiences. The knowledge from humans and
RL is unified and used for dynamically computing task-specific planning models
under potentially new environments. Experiments were conducted using a mobile
robot working on dialog, navigation, and delivery tasks. Results show
significant improvements, in comparison to existing model-based RL methods.
