Q-value Regularized Decision ConvFormer for Offline Reinforcement Learning

TL;DR

This paper introduces Q-value Regularized Decision ConvFormer (QDC), a novel offline RL model that combines trajectory modeling with value maximization, leading to improved performance and trajectory stitching on benchmarks.

Contribution

QDC integrates Decision ConvFormer with a Q-value regularization term, enhancing trajectory consistency and decision quality in offline reinforcement learning.

Findings

Outperforms existing methods on D4RL benchmarks.

Demonstrates superior trajectory stitching capabilities.

Achieves near-optimal performance across various environments.

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.