  Many deep reinforcement learning algorithms contain inductive biases that
sculpt the agent's objective and its interface to the environment. These
inductive biases can take many forms, including domain knowledge and pretuned
hyper-parameters. In general, there is a trade-off between generality and
performance when algorithms use such biases. Stronger biases can lead to faster
learning, but weaker biases can potentially lead to more general algorithms.
This trade-off is important because inductive biases are not free; substantial
effort may be required to obtain relevant domain knowledge or to tune
hyper-parameters effectively. In this paper, we re-examine several
domain-specific components that bias the objective and the environmental
interface of common deep reinforcement learning agents. We investigated whether
the performance deteriorates when these components are replaced with adaptive
solutions from the literature. In our experiments, performance sometimes
decreased with the adaptive components, as one might expect when comparing to
components crafted for the domain, but sometimes the adaptive components
performed better. We investigated the main benefit of having fewer
domain-specific components, by comparing the learning performance of the two
systems on a different set of continuous control problems, without additional
tuning of either system. As hypothesized, the system with adaptive components
performed better on many of the new tasks.
