  Model-free reinforcement learning (RL) is a powerful, general tool for
learning complex behaviors. However, its sample efficiency is often
impractically large for solving challenging real-world problems, even with
off-policy algorithms such as Q-learning. A limiting factor in classic
model-free RL is that the learning signal consists only of scalar rewards,
ignoring much of the rich information contained in state transition tuples.
Model-based RL uses this information, by training a predictive model, but often
does not achieve the same asymptotic performance as model-free RL due to model
bias. We introduce temporal difference models (TDMs), a family of
goal-conditioned value functions that can be trained with model-free learning
and used for model-based control. TDMs combine the benefits of model-free and
model-based RL: they leverage the rich information in state transitions to
learn very efficiently, while still attaining asymptotic performance that
exceeds that of direct model-based RL methods. Our experimental results show
that, on a range of continuous control tasks, TDMs provide a substantial
improvement in efficiency compared to state-of-the-art model-based and
model-free methods.
