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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