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Conference Paper
Virtual-to-Real Transfer via Dynamics Models (poster)
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- Authors
- Sooyoung Jang, Youngsung Son
- Issue Date
- 2019-06
- Citation
- International Conference on Mobile Systems, Applications, and Services (MobiSys) 2019, pp.516-517
- Language
- English
- Type
- Conference Paper
- Abstract
- The virtual world is essential for deep reinforcement learning. Since the deep reinforcement learning agent learns the optimal policy by interacting with the environment in a trial and error manner, training the agent in the real world is not only cost expensive and time-consuming but also unsafe. Several researches are ongoing in the field of but not limited to drone, vehicle, and robot arm control as the deep reinforcement learning is proven to be an effective solution to sequential decision-making problems such as Atari games [2] and several board games including Go [4]. Due to the above issue, most of these researches are done in the virtual world that mimics the real world.
- KSP Keywords
- Deep reinforcement learning, Dynamics model, Optimal policy, Real-world, Reinforcement Learning(RL), Robot arm control, Sequential decision-making, Virtual world, board games, learning agent, trial and error