  Learning efficiently from small amounts of data has long been the focus of
model-based reinforcement learning, both for the online case when interacting
with the environment and the offline case when learning from a fixed dataset.
However, to date no single unified algorithm could demonstrate state-of-the-art
results in both settings. In this work, we describe the Reanalyse algorithm
which uses model-based policy and value improvement operators to compute new
improved training targets on existing data points, allowing efficient learning
for data budgets varying by several orders of magnitude. We further show that
Reanalyse can also be used to learn entirely from demonstrations without any
environment interactions, as in the case of offline Reinforcement Learning
(offline RL). Combining Reanalyse with the MuZero algorithm, we introduce
MuZero Unplugged, a single unified algorithm for any data budget, including
offline RL. In contrast to previous work, our algorithm does not require any
special adaptations for the off-policy or offline RL settings. MuZero Unplugged
sets new state-of-the-art results in the RL Unplugged offline RL benchmark as
well as in the online RL benchmark of Atari in the standard 200 million frame
setting.
