  Perfect channel state information (CSI) is usually required when considering
relay selection and power allocation in cooperative communication. However, it
is difficult to get an accurate CSI in practical situations. In this letter, we
study the outage probability minimizing problem based on optimizing relay
selection and transmission power. We propose a prioritized experience replay
aided deep deterministic policy gradient learning framework, which can find an
optimal solution by dealing with continuous action space, without any prior
knowledge of CSI. Simulation results reveal that our approach outperforms
reinforcement learning based methods in existing literatures, and improves the
communication success rate by about 4%.
