Analyzing and visualizing multiagent rewards in dynamic and stochastic domains

Author:

Agogino Adrian K.,Tumer Kagan

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference32 articles.

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2. Agogino, A., Martin, C., & Ghosh, J. (1999). Visualization of radial basis function networks. In Proceedings of International Joint Conference on Neural Networks. Washington, DC.

3. Agogino, A., & Tumer, K. (2004). Efficient evaluation functions for multi-rover systems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004) (pp. 1–12). Seattle, WA.

4. Agogino, A., & Tumer, K. (2005). Multi agent reward analysis for learning in noisy domains. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems, Utrecht, Netherlands.

5. Baird, L., & Moore, A. (1999). Gradient descent for general reinforcement learning. In Advances in Neural Information Processing Systems (NIPS) (pp. 968–974). Cambridge, MA.

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