By Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov (eds.)
Adaptive brokers and Multi-Agent platforms is an rising and fascinating interdisciplinary zone of study and improvement concerning man made intelligence, desktop technological know-how, software program engineering, and developmental biology, in addition to cognitive and social science.
This ebook surveys the state-of-the-art during this rising box via drawing jointly completely chosen reviewed papers from similar workshops; in addition to papers by way of major researchers particularly solicited for this e-book. The articles are prepared into topical sections on
- studying, cooperation, and communication
- emergence and evolution in multi-agent systems
- theoretical foundations of adaptive agents
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Extra resources for Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning
S. Singh, T. Jaakkola, M. L. Littman, and C Szpesvari. Convergence results for single-step on-policy reinforcement-learning algorithms. Machine Learning Journal, 38(3):287–308, 2000. 9. Ming Tan. Multi-agent reinforcement learning: Independent vs. cooperative agents. In Proceedings of the Tenth International Conference on Machine Learning, pages 330–337, 1993. 10. C. J. C. H. Watkins. Learning from Delayed Rewards. PhD thesis, Cambridge University, Cambridge, England, 1989. 11. Gerhard Weiss. Learning to coordinate actions in multi-agent systems.
In contrast, low temperature values encourage exploitation. The value of the temperature is typically decreased over time from an initial value as exploitation takes over from exploration until it reaches some designated lower limit. The three important settings for the temperature are the initial value, the rate of decrease and the number of steps until it reaches its lowest limit. The lower limit of the temperature needs to be set to a value that is close enough to 0 to allow the learners to converge by stopping their exploration.
All agents are started at the same time and run synchronously. Considering the several possibilities for exchanging information regarding the learning process, discussed in the previous section, this option seemed the most promising for the following reasons: a) Sharing of episodes does not put heavy restrictions on the heterogeneity of the underlying learning algorithms and may be achieved using a simple communication mechanism; b) Having different algorithms solving similar problems may lead to different forms of exploration of the same search space, thus increasing the probability of finding a good solution; c) It is more informative and less dependent on pre-coded knowledge than the exchange of environment’s states.