My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control (2010.01856v2)

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.

• The paper challenges the assumption that GNNs effectively leverage morphological data, showing that explicit encoding does not enhance control performance.
• It introduces Amorpheus, a transformer-based approach that bypasses multi-hop message passing through attention-based aggregation.
• Experimental results reveal that Amorpheus outperforms GNNs in sample efficiency and overall performance on tasks with incompatible state-action spaces.

Analysis of "My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control"

The paper "My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control" presents an exploration into the application of Graph Neural Networks (GNNs) and transformers within the context of Multitask Reinforcement Learning (MTRL). The authors critically evaluate the prevailing assumption that GNNs can effectively leverage morphological information to enhance performance in continuous control tasks across varying state-action dimensions.

Background and Motivation

Traditional MTRL approaches assume compatibility across tasks, facilitating shared state-action space dimensions. However, this paper addresses the challenges posed by incompatible environments, where this assumption does not hold. These environments are significant in fields such as robotics and optimization.

The introduction of GNNs in such settings allows for processing graphs of arbitrary size, purportedly offering an advantage by encoding physical morphology. However, the authors question the efficacy of leveraging such structural biases, proposing that the inherent difficulties in message passing within GNNs may negate any potential benefits.

Methodology

A series of ablations were conducted to discern the impact of morphological information encoded within GNNs. The paper concluded that such morphological encoding does not enhance performance. This key finding motivated the introduction of a novel transformer-based approach, named \textit{Amorpheus}.

Unlike GNNs, \textit{Amorpheus} does not rely on morphological information to define its message-passing scheme. Instead, it employs transformers, allowing for fully connected operations with attention-based aggregation. This design choice mitigates the need for multi-hop communication and leverages the self-attention mechanism to potentially uncover implicit structural relationships within the data.

Numerical Results and Findings

The experiments demonstrated that \textit{Amorpheus} surpasses existing GNN-based methods in terms of sample efficiency and overall performance across a variety of continuous control benchmarks, including Walker++, Cheetah++, and Humanoid++ environments. These environments feature incompatible state-action spaces, highlighting the robustness of the transformer-based strategy.

Specifically, \textit{Amorpheus} achieved substantial improvements in environments where GNN-based models struggled, such as the Cheetah-Walker-Humanoid++ benchmark. The approach exhibited complex attention patterns synchronized with agent gaits, providing insights into the underlying mechanisms of control tasks.

Implications and Future Work

This work challenges the established notion that morphological information provides a decisive advantage in GNN-based MTRL. The findings suggest that the flexibility and adaptability inherent in transformer architectures make them well-suited for handling the variances present in incompatible environments.

In terms of broader implications, \textit{Amorpheus} opens pathways for optimizing policy architectures in scenarios where explicit morphological modeling is either infeasible or non-beneficial. Future research may extend this work by exploring efficiency improvements in transformer-based models, particularly addressing their quadratic complexity.

Moreover, further investigations could probe the potential for integrating other learning algorithms to enhance task balancing and optimization within MTRL frameworks. By exploiting the capabilities of transformers in learning implicit structures, future developments might achieve even greater synergy between architecture design and task-specific generalization.