  A recent method for solving zero-sum partially observable stochastic games
(zs-POSGs) embeds the original game into a new one called the occupancy Markov
game. This reformulation allows applying Bellman's principle of optimality to
solve zs-POSGs. However, improving a current solution requires solving a linear
program with exponentially many potential constraints, which significantly
restricts the scalability of this approach. This paper exploits the optimal
value function's novel uniform continuity properties to overcome this
limitation. We first construct a new operator that is computationally more
efficient than the state-of-the-art update rules without compromising
optimality. In particular, improving a current solution now involves a linear
program with an exponential drop in constraints. We then also show that
point-based value iteration algorithms utilizing our findings improve the
scalability of existing methods while maintaining guarantees in various
domains.
