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ICANN
2001
Springer
14 years 2 months ago
Market-Based Reinforcement Learning in Partially Observable Worlds
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an ...
Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
ATAL
2007
Springer
14 years 4 months ago
Batch reinforcement learning in a complex domain
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
Shivaram Kalyanakrishnan, Peter Stone
AAAI
2007
14 years 8 days ago
Optimizing Anthrax Outbreak Detection Using Reinforcement Learning
The potentially catastrophic impact of a bioterrorist attack makes developing effective detection methods essential for public health. In the case of anthrax attack, a delay of ho...
Masoumeh T. Izadi, David L. Buckeridge
AAAI
2008
14 years 8 days ago
Reinforcement Learning for Vulnerability Assessment in Peer-to-Peer Networks
Proactive assessment of computer-network vulnerability to unknown future attacks is an important but unsolved computer security problem where AI techniques have significant impact...
Scott Dejmal, Alan Fern, Thinh Nguyen
AAAI
2012
12 years 10 days ago
Kernel-Based Reinforcement Learning on Representative States
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Branislav Kveton, Georgios Theocharous