Recently, a state-of-the-art series of algorithms—Goal-Conditioned Weighted Supervised Learning (GCWSL) methods—has been introduced to address the challenges inherent in offline goal-conditioned reinforcement learning (RL). GCWSL optimizes a lower bound on the goal-conditioned RL objective and has demonstrated exceptional performance across a range of goal-reaching tasks, offering a simple, effective, and stable solution. Nonetheless, researches has revealed a critical limitation in GCWSL: the absence of trajectory stitching capabilities. In response, goal data augmentation strategies have been proposed to enhance these methods. However, existing techniques often fail to effectively sample appropriate augmented goals for GCWSL. In this paper, we establish unified principles for goal data augmentation, emphasizing goal diversity, action optimality, and goal reachability. Building on these principles, we propose a Model-based Goal Data Augmentation (MGDA) approach, which leverages a dynamics model to sample more appropriate augmented goals. MGDA uniquely incorporates the local Lipschitz continuity assumption within the learned model to mitigate the effects of compounding errors. Empirical results demonstrate that MGDA significantly improves the performance of GCWSL methods on both state-based and vision-based maze datasets, outperforming previous goal data augmentation techniques in their ability to enhancing stitching capabilities.

In this paper, we explore how to design lightweight CNN architecture for embedded computing systems. We propose L-Mobilenet model for ZYNQ based hardware platform. L-Mobilenet can adapt well to hardware computing and accelerating, and its network structure is inspired by the state-of-the-art work of Inception-Resnet and Mobilenet-V2, which can effectively reduce parameters and delay while maintaining the accuracy of inference. We deploy our L-Mobilenet model to GPU and ZYNQ embedded platform for fully evaluating the performance of our design. By measuring with cifar10 and cifar100 datasets, L-Mobilenet model is able to gain 3× speed up and 3.7× fewer parameters than MobileNet-V2 while maintaining a similar accuracy. It also can obtain 2× speed up and 1.5× fewer parameters than Shufflenet-V2 while maintaining the same accuracy. Experiments show that our network model can obtain better performance because of the special considerations for hardware accelerating and software-hardware co-design strategies in our L-Mobilenet bottleneck architecture.