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» Reinforcement learning for games: failures and successes
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CEC
2010
IEEE
13 years 6 months ago
Learning to overtake in TORCS using simple reinforcement learning
In modern racing games programming non-player characters with believable and sophisticated behaviors is getting increasingly challenging. Recently, several works in the literature ...
Daniele Loiacono, Alessandro Prete, Pier Luca Lanz...
AAAI
2010
13 years 6 months ago
Bayesian Policy Search for Multi-Agent Role Discovery
Bayesian inference is an appealing approach for leveraging prior knowledge in reinforcement learning (RL). In this paper we describe an algorithm for discovering different classes...
Aaron Wilson, Alan Fern, Prasad Tadepalli
ECAI
2006
Springer
13 years 8 months ago
Strategic Foresighted Learning in Competitive Multi-Agent Games
We describe a generalized Q-learning type algorithm for reinforcement learning in competitive multi-agent games. We make the observation that in a competitive setting with adaptive...
Pieter Jan't Hoen, Sander M. Bohte, Han La Poutr&e...
ISDA
2010
IEEE
13 years 2 months ago
Intelligent online case-based planning agent model for real-time strategy games
Research in learning and planning in real-time strategy (RTS) games is very interesting in several industries such as military industry, robotics, and most importantly game industr...
Ibrahim Fathy, Mostafa Aref, Omar Enayet, Abdelrah...
NIPS
1996
13 years 6 months ago
Why did TD-Gammon Work?
Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning for other applications or ...
Jordan B. Pollack, Alan D. Blair