  In the past few years, off-policy reinforcement learning methods have shown
promising results in their application for robot control. Deep Q-learning,
however, still suffers from poor data-efficiency and is susceptible to
stochasticity in the environment or reward functions which is limiting with
regard to real-world applications. We alleviate these problems by proposing two
novel off-policy Temporal-Difference formulations: (1) Truncated Q-functions
which represent the return for the first n steps of a target-policy rollout
w.r.t. the full action-value and (2) Shifted Q-functions, acting as the
farsighted return after this truncated rollout. This decomposition allows us to
optimize both parts with their individual learning rates, achieving significant
learning speedup. We prove that the combination of these short- and long-term
predictions is a representation of the full return, leading to the Composite
Q-learning algorithm. We show the efficacy of Composite Q-learning in the
tabular case and compare Deep Composite Q-learning with TD3 and TD3(Delta),
which we introduce as an off-policy variant of TD(Delta). Moreover, we show
that Composite TD3 outperforms TD3 as well as state-of-the-art compositional
Q-learning approaches significantly in terms of data-efficiency in multiple
simulated robot tasks and that Composite Q-learning is robust to stochastic
environments and reward functions.
