Related papers: Q-value Regularized Decision ConvFormer for Offline Reinforcement Learning

Q-value Regularized Decision ConvFormer for Offline Reinforcement Learning

URL: http://arxiv.org/abs/2409.08062v1
Date: Thu, 12 Sep 2024 14:10:22 GMT
Title: Q-value Regularized Decision ConvFormer for Offline Reinforcement Learning
Authors: Teng Yan, Zhendong Ruan, Yaobang Cai, Yu Han, Wenxian Li, Yang Zhang,
Abstract summary: Decision Transformer (DT) has demonstrated exceptional capabilities in offline reinforcement learning. Decision ConvFormer (DC) is easier to understand in the context of modeling RL trajectories within a Markov Decision Process. We propose the Q-value Regularized Decision ConvFormer (QDC), which combines the understanding of RL trajectories by DC and incorporates a term that maximizes action values.
Score: 5.398202201395825
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: As a data-driven paradigm, offline reinforcement learning (Offline RL) has been formulated as sequence modeling, where the Decision Transformer (DT) has demonstrated exceptional capabilities. Unlike previous reinforcement learning methods that fit value functions or compute policy gradients, DT adjusts the autoregressive model based on the expected returns, past states, and actions, using a causally masked Transformer to output the optimal action. However, due to the inconsistency between the sampled returns within a single trajectory and the optimal returns across multiple trajectories, it is challenging to set an expected return to output the optimal action and stitch together suboptimal trajectories. Decision ConvFormer (DC) is easier to understand in the context of modeling RL trajectories within a Markov Decision Process compared to DT. We propose the Q-value Regularized Decision ConvFormer (QDC), which combines the understanding of RL trajectories by DC and incorporates a term that maximizes action values using dynamic programming methods during training. This ensures that the expected returns of the sampled actions are consistent with the optimal returns. QDC achieves excellent performance on the D4RL benchmark, outperforming or approaching the optimal level in all tested environments. It particularly demonstrates outstanding competitiveness in trajectory stitching capability.