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Machine Learning Proceedings 1994
Markov games as a framework for multi-agent reinforcement learning
edited_book
Author(s):
Michael L. Littman
Publication date
(Print):
1994
Publisher:
Elsevier
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Open source discrete and agent-based modeling frameworks for biology
Author and book information
Book Chapter
Publication date (Print):
1994
Pages
: 157-163
DOI:
10.1016/B978-1-55860-335-6.50027-1
SO-VID:
68b551b0-badf-42a3-b83c-875feb4d84f1
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https://www.elsevier.com/tdm/userlicense/1.0/
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Book chapters
pp. 19
Improving Accuracy of Incorrect Domain Theories
pp. 28
Greedy Attribute Selection
pp. 37
Using Sampling and Queries to Extract Rules from Trained Neural Networks
pp. 53
Boosting and Other Machine Learning Algorithms
pp. 62
In Defense of C4.5: Notes on Learning One-Level Decision Trees
pp. 70
Incremental Reduced Error Pruning
pp. 105
Consideration of Risk in Reinforcement Learning
pp. 121
Irrelevant Features and the Subset Selection Problem
pp. 148
Heterogeneous Uncertainty Sampling for Supervised Learning
pp. 157
Markov games as a framework for multi-agent reinforcement learning
pp. 181
Reward Functions for Accelerated Learning
pp. 190
Efficient Algorithms for Minimizing Cross Validation Error
pp. 217
Reducing Misclassification Costs
pp. 226
Incremental Multi-Step Q-Learning
pp. 259
A Conservation Law for Generalization Performance
pp. 284
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
pp. 293
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
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