Deep reinforcement learning has the potential to train robots to perform
complex tasks in the real world without requiring accurate models of the robot
or its environment. A practical approach is to train agents in simulation, and
then transfer them to the real world. One popular method for achieving
transferability is to use domain randomisation, which involves randomly
perturbing various aspects of a simulated environment in order to make trained
agents robust to the reality gap. However, less work has gone into
understanding such agents - which are deployed in the real world - beyond task
performance. In this work we examine such agents, through qualitative and
quantitative comparisons between agents trained with and without visual domain
randomisation. We train agents for Fetch and Jaco robots on a visuomotor
control task and evaluate how well they generalise using different testing
conditions. Finally, we investigate the internals of the trained agents by
using a suite of interpretability techniques. Our results show that the primary
outcome of domain randomisation is more robust, entangled representations,
accompanied with larger weights with greater spatial structure; moreover, the
types of changes are heavily influenced by the task setup and presence of
additional proprioceptive inputs. Additionally, we demonstrate that our domain
randomised agents require higher sample complexity, can overfit and more
heavily rely on recurrent processing. Furthermore, even with an improved
saliency method introduced in this work, we show that qualitative studies may
not always correspond with quantitative measures, necessitating the combination
of inspection tools in order to provide sufficient insights into the behaviour
of trained agents.