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Conference Paper
Reinforcement Learning of Intelligent Characters in Fighting Action Games
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- Authors
- Byeong Heon Cho, Sung Hoon Jung, Kwang-Hyun Shim, Yeong Rak Seong, Ha Ryoung Oh
- Issue Date
- 2006-09
- Citation
- International Conference on Entertainment Computing (ICEC) 2006 (LNCS 4161), v.4161, pp.310-313
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we investigate reinforcement learning (RL) of intelligent characters, based on neural network technology, for fighting action games. RL can be either on-policy or off-policy. We apply both schemes to tabula rasa learning and adaptation. The experimental results show that (1) in tabula rasa leaning, off-policy RL outperforms on-policy RL, but (2) in adaptation, on-policy RL outperforms off-policy RL. © IFIP International Federation for Information Processing 2006.
- KSP Keywords
- Action Games, International federation, Learning and adaptation, Network technology, Reinforcement Learning(RL), information processing, neural network, off-policy