- 초록
- Reinforcement learning is a type of machine learning paradigm that forces agents to repeat the observation-action-reward process to assess and predict the values of possible future action sequences. This allows the agents to incrementally reinforce the desired behavior for a given observation. Thanks to the recent advancements of deep learning, reinforcement learning has evolved into deep reinforcement learning that introduces promising results in various control and optimization domains, such as games, robotics, autonomous vehicles, computing, industrial control, and so on. In addition to this trend, a number of programming libraries have been developed for importing deep reinforcement learning into a variety of applications. In this article, we briefly review and summarize 10 representative deep reinforcement learning libraries and compare them from a development project perspective.
- 저자
- 신승재지능네트워크연구실sjshin0505@etri.re.kr
조충래지능네트워크연구실clcho@etri.re.kr
전홍석지능네트워크연구실jeonhs@etri.re.kr
윤승현지능네트워크연구실shpyoon@etri.re.kr
김태연지능네트워크연구실tykim@etri.re.kr
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