  Reinforcement learning (RL) provides an appealing formalism for learning
control policies from experience. However, the classic active formulation of RL
necessitates a lengthy active exploration process for each behavior, making it
difficult to apply in real-world settings such as robotic control. If we can
instead allow RL algorithms to effectively use previously collected data to aid
the online learning process, such applications could be made substantially more
practical: the prior data would provide a starting point that mitigates
challenges due to exploration and sample complexity, while the online training
enables the agent to perfect the desired skill. Such prior data could either
constitute expert demonstrations or sub-optimal prior data that illustrates
potentially useful transitions. While a number of prior methods have either
used optimal demonstrations to bootstrap RL, or have used sub-optimal data to
train purely offline, it remains exceptionally difficult to train a policy with
offline data and actually continue to improve it further with online RL. In
this paper we analyze why this problem is so challenging, and propose an
algorithm that combines sample efficient dynamic programming with maximum
likelihood policy updates, providing a simple and effective framework that is
able to leverage large amounts of offline data and then quickly perform online
fine-tuning of RL policies. We show that our method, advantage weighted actor
critic (AWAC), enables rapid learning of skills with a combination of prior
demonstration data and online experience. We demonstrate these benefits on
simulated and real-world robotics domains, including dexterous manipulation
with a real multi-fingered hand, drawer opening with a robotic arm, and
rotating a valve. Our results show that incorporating prior data can reduce the
time required to learn a range of robotic skills to practical time-scales.
