  We present a method for using previously-trained 'teacher' agents to
kickstart the training of a new 'student' agent. To this end, we leverage ideas
from policy distillation and population based training. Our method places no
constraints on the architecture of the teacher or student agents, and it
regulates itself to allow the students to surpass their teachers in
performance. We show that, on a challenging and computationally-intensive
multi-task benchmark (DMLab-30), kickstarted training improves the data
efficiency of new agents, making it significantly easier to iterate on their
design. We also show that the same kickstarting pipeline can allow a single
student agent to leverage multiple 'expert' teachers which specialize on
individual tasks. In this setting kickstarting yields surprisingly large gains,
with the kickstarted agent matching the performance of an agent trained from
scratch in almost 10x fewer steps, and surpassing its final performance by 42
percent. Kickstarting is conceptually simple and can easily be incorporated
into reinforcement learning experiments.
