  Multitask Reinforcement Learning is a promising way to obtain models with
better performance, generalisation, data efficiency, and robustness. Most
existing work is limited to compatible settings, where the state and action
space dimensions are the same across tasks. Graph Neural Networks (GNN) are one
way to address incompatible environments, because they can process graphs of
arbitrary size. They also allow practitioners to inject biases encoded in the
structure of the input graph. Existing work in graph-based continuous control
uses the physical morphology of the agent to construct the input graph, i.e.,
encoding limb features as node labels and using edges to connect the nodes if
their corresponded limbs are physically connected. In this work, we present a
series of ablations on existing methods that show that morphological
information encoded in the graph does not improve their performance. Motivated
by the hypothesis that any benefits GNNs extract from the graph structure are
outweighed by difficulties they create for message passing, we also propose
Amorpheus, a transformer-based approach. Further results show that, while
Amorpheus ignores the morphological information that GNNs encode, it
nonetheless substantially outperforms GNN-based methods that use the
morphological information to define the message-passing scheme.
