|Website:||website containing additional information|
|Period:||period 2 (week 46 through 5, i.e., 13-11-2017 through 2-2-2018; retake week 16)
|Participants:||up till now 45 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|week: 5||Thu 1-2-2018||8.30-11.30 uur||room: BBG-001|
|week: 5||Thu 1-2-2018||8.30-11.30 uur||room: BBG-023|
|week: 16||Thu 19-4-2018||8.30-11.30 uur||room: BBG-001||retake exam|
|Contents:||This course discusses forms of machine learning that typically occur in multi-agent systems. Topics: learning and teaching, fictitious play, rational learning, no-regret learning, multi-agent reinforcement learning, evolutionary learning.
The course assumes knowledge of probability theory and game theory.
|Literature:||Available through the course site.
Download and print of material is a responsibility of the student.|
|Exam form:||Midterm / final exam (70%), two programming assignments (30%).|
|Minimum effort to qualify for 2nd chance exam:||Average 4.|
|Description:||Multi-agent learning (MAL) studies software agents that learn and adapt to the behaviour of other software agents, that themselves adapt to the behaviour of other software agents. The presence of other learning agents complicates learning, which makes the environment non-stationary (a situation of learning a moving target) and non-Markovian (a situation where not only experiences from the immediate past but also earlier experiences are relevant). With adaptive agents it also becomes less beneficent to only adapt to the behaviour of other agents, on the pain of being exploited by more steadfast agents that do not follow but instead impose their strategy on others. Important topics of adaptive agents include statistical learning and single-agent reinforcement learning. Important topics of MAL include (evolutionary) game theory, fictitious play, gradient dynamics, no-regret learning, multi-agent reinforcement learning (MinMax-Q, Nash-Q), leader (teacher) vs. follower (learner) adaptation, and the emergence of social conventions. Examples of domains that need robust MAL algorithms include manufacturing systems (managers of a factory coordinate to maximise their profit), distributed sensor networks (multiple sensors collaborate to perform a large-scale sensing task under strict power constraints), robo-soccer, disaster rescue (robots must safely find victims as fast as possible after an earthquake) and recreational games of imperfect information such as poker. Indeed, poker and simplified forms of poker are an important topics of research in multi-agent learning.|