Congestion games offer a perfect environment in which to study the impact of local decisions on global utilities in multiagent systems. What is particularly interesting in such problems is that no individual action is intrinsically “good” or “bad” but that combinations of actions lead to desirable or undesirable outcomes. As...
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high
stochasticity or "outcome space" explosion. Multiagent domains are particularly susceptible to these problems. This thesis describes ways to mitigate these curses in several different multiagent domains, including real-time delivery of products...