Discounted Value of a Zero-Sum Stochastic Game
For a two-player zero-sum stochastic game ([[game_theory.stochastic_game.core.stochastic_game]]) with discount factor $\gamma \in (0, 1)$, the $\gamma$-discounted payoff of a play $(s_0, a_0, s_1, a_1, \dots)$ is $$ \Pi_\gamma = (1 - \gamma) \sum_{t = 0}^{\infty} \gamma^t \, r(s_t, a_t). $$
The factor $(1 - \gamma)$ normalises so that constant per-stage payoffs average to themselves, putting $\Pi_\gamma$ on the same scale as the stage payoff $r$.
$\gamma$-discounted value
The $\gamma$-discounted value $v_\gamma(s)$ at initial state $s$ is the value of the (zero-sum) game with payoff $\mathbb{E}_\sigma [\Pi_\gamma | s_0 = s]$: $$ v_\gamma(s) = \sup_{\sigma_1} \inf_{\sigma_2}\, \mathbb{E}_{\sigma_1, \sigma_2}[\Pi_\gamma \mid s_0 = s] = \inf_{\sigma_2} \sup_{\sigma_1}\, \mathbb{E}_{\sigma_1, \sigma_2}[\Pi_\gamma \mid s_0 = s]. $$
The minimax equality on the RHS is part of Shapley's theorem and is not a definition; it is a consequence of $v_\gamma$ being the unique fixed point of the Shapley operator ([[game_theory.stochastic_game.value.shapley_operator]], [[game_theory.stochastic_game.value.discounted_value_fixed_point]]).
Stationary optimal strategies exist
Shapley 1953 proves that for finite-state, finite-action zero-sum stochastic games, optimal strategies of both players can be chosen stationary — depending only on the current state, not on the entire history. This drops the strategy space from doubly-exponential in the horizon to a Cartesian product of $|S|$ matrix-game mixed strategies.
Range and continuity
- $v_\gamma : S \to \mathbb{R}$ is bounded by $\|r\|_\infty$.
- $\gamma \mapsto v_\gamma$ is rational in $\gamma$ (semialgebraic), a fact that fuels Bewley-Kohlberg's asymptotic value theorem ([[game_theory.stochastic_game.asymptotic.bewley_kohlberg_asymptotic_value]]).
Special cases
- Repeated game (1 state, no transitions): $v_\gamma$ equals the matrix-game value of the stage game, independent of $\gamma$.
- MDP (1 player): $v_\gamma$ is the standard discounted MDP value, and the Shapley operator reduces to the Bellman operator.
References
- Shapley, L. S. (1953). "Stochastic Games". PNAS 39: 1095–1100.
- [MFoGT Chapter 8, §8.6.2] Laraki, Renault, and Sorin, Mathematical Foundations of Game Theory.