RL-DOT: A Reinforcement Learning NPC Team for Playing Domination Games.
Hao WangYang GaoXingguo ChenPublished in: IEEE Trans. Comput. Intell. AI Games (2010)
Keyphrases
- reinforcement learning
- game playing
- computer games
- game players
- games played
- temporal difference learning
- foreign language learners
- learning agents
- imperfect information
- board game
- online game
- reinforcement learning agents
- reinforcement learning algorithms
- function approximation
- rl algorithms
- state space
- game design
- two player games
- video games
- multi player
- robocup soccer
- learning algorithm
- model free
- card game
- general game playing
- machine learning
- card games
- optimal policy
- markov decision processes
- temporal difference
- function approximators
- action space
- nash equilibria
- digital games
- team members
- computer poker
- dynamic programming
- robotic soccer
- action selection
- multi agent
- playing games
- complex domains
- learning process
- minimax search
- actor critic
- multi agent reinforcement learning
- perfect information
- educational games
- serious games
- reinforcement learning methods
- real robot
- policy evaluation
- game tree
- reward shaping
- game theory
- human players
- partially observable