Q-learning in Continuous Action Space by Extending EVA

Today, in order to use reinforcement learning in the real world, we need to improve the efficiency of reinforcement learning in continuous action spaces. In this study, we propose Q-learning in continuous action space based on an agent's past series of experiences (i.e., trajectory information). We evaluate the effectiveness of our proposed method by conducting comparative experiments on several tasks.