MY BODY IS A CAGE: THE ROLE OF MORPHOLOGY IN GRAPH-BASED INCOMPATIBLE CONTROL

ABSTRACT

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.

1. INTRODUCTION

Multitask Reinforcement Learning (MTRL) (Vithayathil Varghese & Mahmoud, 2020) leverages commonalities between multiple tasks to obtain policies with better returns, generalisation, data efficiency, or robustness. Most MTRL work assumes compatible state-action spaces, where the dimensionality of the states and actions is the same across tasks. However, many practically important domains, such as robotics, combinatorial optimization, and object-oriented environments, have incompatible state-action spaces and cannot be solved by common MTRL approaches. Incompatible environments are avoided largely because they are inconvenient for function approximation: conventional architectures expect fixed-size inputs and outputs. One way to overcome this limitation is to use Graph Neural Networks (GNNs) (Gori et al., 2005; Scarselli et al., 2005; Battaglia et al., 2018) . A key feature of GNNs is that they can process graphs of arbitrary size and thus, in

