Graph Decision Transformer

• URL: http://arxiv.org/abs/2303.03747v1
• Date: Tue, 7 Mar 2023 09:10:34 GMT
• Title: Graph Decision Transformer
• Authors: Shengchao Hu, Li Shen, Ya Zhang, Dacheng Tao
• Abstract summary: Graph Decision Transformer (GDT) is a novel offline reinforcement learning approach. GDT models the input sequence into a causal graph to capture potential dependencies between fundamentally different concepts. 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.
• Score: 83.76329715043205
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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