  Deep Reinforcement Learning is a promising paradigm for robotic control which
has been shown to be capable of learning policies for high-dimensional,
continuous control of unmodeled systems. However, RoboticReinforcement Learning
currently lacks clearly defined benchmark tasks, which makes it difficult for
researchers to reproduce and compare against prior work. ``Reacher'' tasks,
which are fundamental to robotic manipulation, are commonly used as benchmarks,
but the lack of a formal specification elides details that are crucial to
replication. In this paper we present a novel empirical analysis which shows
that the unstated spatial constraints in commonly used implementations of
Reacher tasks make it dramatically easier to learn a successful control policy
with DeepDeterministic Policy Gradients (DDPG), a state-of-the-art Deep RL
algorithm. Our analysis suggests that less constrained Reacher tasks are
significantly more difficult to learn, and hence that existing de facto
benchmarks are not representative of the difficulty of general robotic
manipulation.
