Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision | ||
|
public:research:index [2017/11/03 14:26] kimkj |
public:research:index [2019/08/01 17:34] (current) |
||
|---|---|---|---|
| Line 2: | Line 2: | ||
| ===== Research on Cognitive Models, Deep Architecture and Multimodal Representation for Deep Reinforcement Learning to Solve Complex Video Games ===== | ===== Research on Cognitive Models, Deep Architecture and Multimodal Representation for Deep Reinforcement Learning to Solve Complex Video Games ===== | ||
| - | {{:public:research:drl_project.jpg|direct&800|}} | + | |
| - | {{:public:research:drl_project.jpg?direct&400|}} | + | {{:public:research:drl_project_small.jpg|}} |
| + | |||
| + | ===== Summary ===== | ||
| + | Deep Reinforcement Learning (DRL) integrates deep learning models into reinforcement learning framework to approximate action/state value or policy functions. Although it has been studied just for 3 years since 2013, the approach has produced outstanding results such as playing Atari video games at human level (Nature 2015) and beating the grand master of Go with AlphaGo (Nature 2016). Recently, there has been big demand on improving the deep reinforcement learning to tackle complex video games as an essential step towards real-world applications in robotics, conversational agents and recommendation systems. In this project, we propose to study cognitive models, deep architectures, and multimodal representation for advanced deep reinforcement learning. | ||
| + | |||
| ===== Research on Computational Intelligence Techniques for Adaptive Video Games ===== | ===== Research on Computational Intelligence Techniques for Adaptive Video Games ===== | ||
