Abstract Reinforcement learning is a major tool to realize intelligent agents that can be autonomously adaptive to the environment. With deep models, reinforcement learning has shown great potential in complex tasks such as playing games from pixels. However, current reinforcement learning techniques are still suffer from requiring a huge amount of interaction data, which could result in unbearable cost in realworld applications. In this article, we share our understanding of the problem, and discuss possible ways to alleviate the sample cost of reinforcement learning, from the aspects of exploration, optimization, environment modeling, experience transfer, and abstraction. We also discuss some challenges in real-world applications, with the hope of inspiring future researches.