  Deep reinforcement learning (DRL) has shown remarkable success in sequential
decision-making problems but suffers from a long training time to obtain such
good performance. Many parallel and distributed DRL training approaches have
been proposed to solve this problem, but it is difficult to utilize them on
resource-limited devices. In order to accelerate DRL in real-world edge
devices, memory bandwidth bottlenecks due to large weight transactions have to
be resolved. However, previous iterative pruning not only shows a low
compression ratio at the beginning of training but also makes DRL training
unstable. To overcome these shortcomings, we propose a novel weight compression
method for DRL training acceleration, named group-sparse training (GST). GST
selectively utilizes block-circulant compression to maintain a high weight
compression ratio during all iterations of DRL training and dynamically adapt
target sparsity through reward-aware pruning for stable training. Thanks to the
features, GST achieves a 25 \%p $\sim$ 41.5 \%p higher average compression
ratio than the iterative pruning method without reward drop in Mujoco
Halfcheetah-v2 and Mujoco humanoid-v2 environment with TD3 training.
