Enhancing Cross-domain Pre-Trained Decision Transformers with Adaptive Attention

• URL: http://arxiv.org/abs/2409.06985v1
• Date: Wed, 11 Sep 2024 03:18:34 GMT
• Title: Enhancing Cross-domain Pre-Trained Decision Transformers with Adaptive Attention
• Authors: Wenhao Zhao, Qiushui Xu, Linjie Xu, Lei Song, Jinyu Wang, Chunlai Zhou, Jiang Bian,
• Abstract summary: Cross-domain pre-training of decision transformers (DT) has generated significant attention in offline reinforcement learning (Offline RL) Motivated by our analysis, we propose a general method GPT-DTMA, which equips a pre-trained DT with Mixture of Attention (MoA) Experiments demonstrate that GPT-DTMA achieves superior performance in short-term environments compared to baselines, and in long-term environments, it mitigates the negative impact caused by Markov Matrix.
• Score: 10.631495275246428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, the pre-training of decision transformers (DT) using a different domain, such as natural language text, has generated significant attention in offline reinforcement learning (Offline RL). Although this cross-domain pre-training approach achieves superior performance compared to training from scratch in environments required short-term planning ability, the mechanisms by which pre-training benefits the fine-tuning phase remain unclear. Furthermore, we point out that the cross-domain pre-training approach hinders the extraction of distant information in environments like PointMaze that require long-term planning ability, leading to performance that is much worse than training DT from scratch. This work first analyzes these issues and found that Markov Matrix, a component that exists in pre-trained attention heads, is the key to explain the significant performance disparity of pre-trained models in different planning abilities. Inspired by our analysis, we propose a general method GPT-DTMA, which equips a pre-trained DT with Mixture of Attention (MoA), to enable adaptive learning and accommodating diverse attention requirements during fine-tuning. Extensive experiments demonstrate that the effectiveness of GPT-DTMA: it achieves superior performance in short-term environments compared to baselines, and in long-term environments, it mitigates the negative impact caused by Markov Matrix, achieving results comparable to those of DT trained from scratch.

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