Conservative Policy Iteration For Zero-Sum POSGs

The paper introduces an approximate policy‑iteration algorithm for finite‑horizon zero‑sum partially observable stochastic games (zs‑POSGs). It maintains a separate policy for each player and updates both jointly using conservative policy updates along occupancy trajectories, avoiding the need to approximate a full central value function. The method focuses computation on occupancy regions where the current joint policy can be exploited, and shows competitive performance on small horizons while improving the runtime‑exploitability trade‑off at larger horizons compared to existing solvers such as PBVI, HSVI, and CFR+.

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