Reinforcement Learning in Games

By Istvan Szita. 2012

73 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Karthik Narasimhan
2. Shimon Whiteson
3. Sergey Levine
4. Regina Barzilay
5. Diederik Marijn Roijers
6. Tejas D. Kulkarni
7. Jianfeng Gao
8. Peter Vamplew

In Sociology and Political Science

1. Karthik Narasimhan
2. Regina Barzilay
3. Shimon Whiteson
4. Diederik Marijn Roijers
5. Tejas D. Kulkarni
6. Peter Vamplew
7. Richard Dazeley

In Reinforcement Learning

1. Karthik Narasimhan
2. Shimon Whiteson
3. Regina Barzilay
4. Diederik Marijn Roijers
5. Tejas D. Kulkarni
6. Peter Vamplew
7. Richard Dazeley
8. Matthew J. Hausknecht

Citations in the corpus

• Language Understanding for Text-based Games using Deep Reinforcement Learning (2015, methodology): “Q-Learning Reinforcement Learning is a commonly used framework for learning control policies in game environments (Silver et al., 2007; Amato and Shani, 2010; Branavan et al., 2011b; Szita, 2012).”
• A Survey of Multi-Objective Sequential Decision-Making (2013, methodology): “…Markov decision processes (MDPs) (Bellman, 1957a), occur in a range of real-world tasks such as robot control (Kober & Peters, 2012), game playing (Szita, 2012), clinical management of patients (Peek, 1999), military planning (Aberdeen, Thi´ebaux, & Zhang, 2004), and control of elevators (Crites…”
• Applying reinforcement learning to small scale combat in the real-time strategy game StarCraft:Broodwar (2012, unknown): “RL in games is a popular area of application for AI research [4], with the technique being well suited to complex game environments.”