  Markov decision process (MDP) congestion game is an extension of classic
congestion games, where a continuous population of selfish agents solves Markov
decision processes with congestion: the payoff of a strategy decreases as more
population uses it. We draw parallels between key concepts from capacitated
congestion games and MDP. In particular, we show that population mass
constraints in MDP congestion games are equivalent to imposing tolls/incentives
on the reward function, which can be utilized by social planners to achieve
auxiliary objectives. We demonstrate such methods in a simulated Seattle
ride-share model, where tolls and incentives are enforced for two separate
objectives: to guarantee minimum driver density in downtown Seattle, and to
shift the game equilibrium towards a maximum social output.
