56 search results - page 1 / 12 » Reinforcement Learning for Average Reward Zero-Sum Games |
159
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COLT
2004
Springer
15 years 11 months ago
2004
Springer
Abstract. We consider Reinforcement Learning for average reward zerosum stochastic games. We present and analyze two algorithms. The first is based on relative Q-learning and the ...
193
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NIPS
2001
15 years 7 months ago
2001
We consider the problem of learning to attain multiple goals in a dynamic environment, which is initially unknown. In addition, the environment may contain arbitrarily varying ele...
187
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IJCAI
2001
15 years 7 months ago
2001
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
175
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IAT
2008
IEEE
16 years 27 days ago
2008
IEEE
This paper extends the link between evolutionary game theory and multi-agent reinforcement learning to multistate games. In previous work, we introduced piecewise replicator dynam...
177
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ECML
2006
Springer
15 years 10 months ago
2006
Springer
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...