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Nicholas Carboni and Matthew E. Taylor. Preliminary Results for
1 vs. 1 Tactics in Starcraft. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-13), May
2013.
ALA-13
This paper describes the development and analysis of two algorithmsdesigned to allow one agent, the teacher, to give advice toanother agent, the student. These algorithms contribute to a familyof algorithms designed to allow teaching with limited advice.We compare the ability of the student to learn using reinforcementlearning with and without such advice. Experiments are conductedin the Starcraft domain, a challenging but appropriate domain forthis type of research. Our results show that the time at which adviceis given has a significant effect on the result of student learning andthat agents with the best performance in a task may not always bethe most effective teachers.
@inproceedings(ALA13-Carboni, author="Nicholas Carboni and Matthew~E.\ Taylor", title="Preliminary Results for 1 vs.~1 Tactics in Starcraft", Booktitle="Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-13)", month="May", year= "2013", wwwnote={<a href="http://swarmlab.unimaas.nl/ala2013/">ALA-13</a>}, abstract="This paper describes the development and analysis of two algorithms designed to allow one agent, the teacher, to give advice to another agent, the student. These algorithms contribute to a family of algorithms designed to allow teaching with limited advice. We compare the ability of the student to learn using reinforcement learning with and without such advice. Experiments are conducted in the Starcraft domain, a challenging but appropriate domain for this type of research. Our results show that the time at which advice is given has a significant effect on the result of student learning and that agents with the best performance in a task may not always be the most effective teachers.", )
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