Graph Decision Transformer (2303.03747v1)

Abstract: Offline reinforcement learning (RL) is a challenging task, whose objective is to learn policies from static trajectory data without interacting with the environment. Recently, offline RL has been viewed as a sequence modeling problem, where an agent generates a sequence of subsequent actions based on a set of static transition experiences. However, existing approaches that use transformers to attend to all tokens naively can overlook the dependencies between different tokens and limit long-term dependency learning. In this paper, we propose the Graph Decision Transformer (GDT), a novel offline RL approach that models the input sequence into a causal graph to capture potential dependencies between fundamentally different concepts and facilitate temporal and causal relationship learning. GDT uses a graph transformer to process the graph inputs with relation-enhanced mechanisms, and an optional sequence transformer to handle fine-grained spatial information in visual tasks. Our experiments show that GDT matches or surpasses the performance of state-of-the-art offline RL methods on image-based Atari and OpenAI Gym.

Graph Decision Transformer: A Novel Approach in Offline Reinforcement Learning

Introduction

Offline Reinforcement Learning (RL) presents a unique challenge in the domain of artificial intelligence, concerned with devising robust policies not through active interaction with an environment but from pre-collected static datasets. This shift in paradigm addresses the practicality and efficiency issues that traditional online RL faces. Emulating RL as a sequence modeling problem has ushered in an innovative perspective where past experiences furnish the fodder for sequence generation models such as Transformers. Despite the success, significant challenges remain rooted in the naive coupling of state and action tokens, inadvertent information overload hindering dependency learning, and the limitations in preserving detailed spatial relationships when dealing with image inputs.

The Graph Decision Transformer Layer

In response to the aforementioned challenges, the Graph Decision Transformer (GDT) is formulated. GDT re-envisions sequence inputs as causal graphs, effectively discerning and distinguishing between different types of tokens - states, actions, and rewards. The methodology considers the underlying Markovian dynamics, yielding a more nuanced understanding of temporal sequences and causal effects within the data. Central to GDT is the Graph Transformer architecture which incorporates graph representations via node and edge embeddings. These play a pivotal role in capturing the causal and temporal nuances between tokens through a sophisticated mechanism of relation-enhanced attention computation.

The versatility of GDT is further accentuated through an accompanying optional Sequence Transformer for visual tasks requiring intricate spatial information processing. In such tasks, GDT-plus is employed where the Graph Transformer integrates with the Sequence Transformer, enabling the model to harness both coarse and fine-grained spatial details for enhanced action prediction.

Empirical Validation

To establish the prowess of GDT, extensive experiments were conducted, pitting the model against state-of-the-art offline RL methods across diverse benchmarks, including image-based Atari and vector-state OpenAI Gym environments. The results underscore the superiority of GDT, showcasing matching or surpassing performance metrics vis-à-vis leading methodologies. The embodiment of causal graphs as input to Graph Transformers illustrated a discernible edge particularly when employed for action prediction.

Implications and Forward-Look

The proposed GDT framework marks a forward leap in sequence modeling for RL. By directly injecting the Markovian properties into the input and leveraging relational mechanisms, GDT demonstrates enhanced capabilities in deciphering complex dependencies—where simplifying assumptions about independent and identically distributed data often fail. Thus, the framework presents an efficient avenue for offline policy learning without the pitfalls of distributional drift inherent in employing traditional off-policy learning directly on these datasets.

Crucially, this work lays a path for further exploration, with an accent on environments demanding thoughtful consideration of spatial relationships. It also prompts reconsideration of graph structures in capturing the essence of RL tasks, possibly influencing future research directions in disciplines other than RL where modeling sequential data is central. However, the full potential of graph-structured inputs remains to be comprehensively ascertained, particularly in areas where connectivity and temporal dependencies are substantial, such as autonomous navigation, robotics, and beyond.