Massively Parallel Methods for Deep Reinforcement Learning arXiv:1507.04296v2 [cs.LG] 16 Jul 2015 Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam,

Source URL: arxiv.org

Online Evolution in Unreal Tournament 2004 Matthew Patrick, Student Member, IEEE Abstract— Traditional approaches to game AI often feature behaviour that is scripted and predictable. Previous attempts at adaptive AI ha

Source URL: game.itu.dk

• Evolutionary algorithms
• Evolution
• Cybernetics
• Mathematical optimization
• Scientific modeling
• Genetic algorithm
• Evolutionary computation
• Fitness landscape
• Evolution strategy
• Multi-agent system
• Agent-based model
• Reinforcement learning

Social Learning Methods in Board Game Agents Vukosi N. Marivate, Student Member, Tshilidzi Marwala, Senior Member, IEEE this has on the overall performance of the different agents created. This paper focuses on improving

Source URL: www.csse.uwa.edu.au

• Cognitive science
• Cognition
• Artificial intelligence
• Academia
• Multi-agent systems
• Belief revision
• Complex systems theory
• Simulation
• Intelligent agent
• Reinforcement learning
• Q-learning
• Agent-based model

Learning and Knowledge Generation in General Games Shiven Sharma, Ziad Kobti, Scott Goodwin Abstract— General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and in th

Source URL: www.csse.uwa.edu.au

• Machine learning
• Artificial intelligence
• Game artificial intelligence
• Applied mathematics
• Computational neuroscience
• Game theory
• Cybernetics
• Ant colony optimization algorithms
• General game playing
• Reinforcement learning
• Computer Go
• Q-learning

RLPy: A Reinforcement Learning Framework for Education and Research http://acl.mit.edu/rlpy † Alborz Geramifard , Robert H. Klein† , Christoph Dann† , William Dabney∗ , Jonathan P. How† † Laboratory for Infor

Source URL: fam-dann.de

Using Reinforcement Learning for City Site Selection in the Turn-Based Strategy Game Civilization IV Stefan Wender, Ian Watson Abstract— This paper describes the design and implementation of a reinforcement learner bas

Source URL: www.csse.uwa.edu.au

• Windows games
• Reinforcement learning
• Q-learning
• Civilization V
• Civilization IV
• Artificial intelligence
• Machine learning
• Civilization
• Freeciv
• Learning
• AI War: Fleet Command

Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference Michael Wick, Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum Department of Computer Science University of Massachusetts Amherst

Source URL: people.cs.umass.edu

J. Fluid Mech), vol. 789, pp. 726–749. doi:jfmc Cambridge University Press 2016

Source URL: www.mattiagazzola.com

• Fisheries
• Fluid dynamics
• Mathematical analysis
• Dipole
• Electromagnetism
• Potential theory
• Reinforcement learning
• Swarm behaviour
• Shoaling and schooling
• Vorticity
• Q-learning
• Markov decision process

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Source URL: www.csse.uwa.edu.au

• Applied mathematics
• Cybernetics
• Evolution
• Machine learning
• Cognitive science
• Theoretical computer science
• Belief revision
• Reinforcement learning
• Perceptron
• Artificial neural network
• Evolutionary computation
• TDL

Playing FlappyBird with Deep Reinforcement Learning Naveen Appiah Mechanical Engineering Sagar Vare Stanford ICME

Source URL: cs231n.stanford.edu