  Reinforcement learning (RL) enables an agent to learn from trial-and-error
experiences toward achieving long-term goals; automated planning aims to
compute plans for accomplishing tasks using action knowledge. Despite their
shared goal of completing complex tasks, the development of RL and automated
planning has been largely isolated due to their different computational
modalities. Focusing on improving RL agents' learning efficiency, we develop
Guided Dyna-Q (GDQ) to enable RL agents to reason with action knowledge to
avoid exploring less-relevant states. The action knowledge is used for
generating artificial experiences from an optimistic simulation. GDQ has been
evaluated in simulation and using a mobile robot conducting navigation tasks in
a multi-room office environment. Compared with competitive baselines, GDQ
significantly reduces the effort in exploration while improving the quality of
learned policies.
