Merge pull request #1283 from carlosgmartin:docs_mfgs

PiperOrigin-RevId: 688127667 Change-Id: I71b139a8d50eaf157559142076ef53ab849969d9
google-deepmind · Oct 21, 2024 · e216731 · e216731
2 parents 548a5eb + 479704b
commit e216731
Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/docs/games.md b/docs/games.md
@@ -52,10 +52,10 @@ Status                                                                 | Game
 🔶                                                                      | [Mancala](https://en.wikipedia.org/wiki/Kalah)                                                 | 2       | ✅              | ✅            | Players take turns sowing beans on the board and try to capture more beans than the opponent.
 🔶                                                                      | Markov Soccer                                                                                  | 2       | ❌              | ❌            | Agents must take the ball to their goal, and can 'tackle' the opponent by predicting their next move. References: [Littman '94, Markov games as a framework for multi-agent reinforcement learning](https://www2.cs.duke.edu/courses/spring07/cps296.3/littman94markov.pdf). [He et al. '16, Opponent Modeling in Deep Reinforcement Learning](https://arxiv.org/abs/1609.05559).
 🟢                                                                      | [Matching Pennies](https://en.wikipedia.org/wiki/Matching_pennies) (3-player)                  | 3       | ✅              | ❌            | Players must predict and match/oppose another player. Designed to have an unstable Nash equilibrium. References: [Jordan '93](https://www.sciencedirect.com/science/article/abs/pii/S0899825683710225).
-🟢                                                                      | Mean Field Game: crowd modelling                                                               | n/a     | n/a            | n/a          | n/a
-🟢                                                                      | Mean Field Game: crowd modelling 2d                                                            | n/a     | n/a            | n/a          | n/a
+🟢                                                                      | Mean Field Game: crowd modelling                                                               | n/a     | n/a            | n/a          | References: [Scaling up Mean Field Games with Online Mirror Descent](https://arxiv.org/abs/2103.00623), [Scalable Deep Reinforcement Learning Algorithms for Mean Field Games](https://arxiv.org/abs/2203.11973), [Learning in Mean Field Games: A Survey](https://arxiv.org/abs/2205.12944).
+🟢                                                                      | Mean Field Game: crowd modelling 2d                                                            | n/a     | n/a            | n/a          | References: [Scaling up Mean Field Games with Online Mirror Descent](https://arxiv.org/abs/2103.00623), [Scalable Deep Reinforcement Learning Algorithms for Mean Field Games](https://arxiv.org/abs/2203.11973), [Learning in Mean Field Games: A Survey](https://arxiv.org/abs/2205.12944).
 🟢                                                                      | Mean Field Game: linear-quadratic                                                              | n/a     | ❌              | ✅            | Players are uniformly distributed and are then incentivized to gather at the same point (The lower the distanbce wrt. the distribution mean position, the higher the reward). A mean-reverting term pushes the players towards the distribution, a gaussian noise term perturbs them. The players' actions alter their states linearly (alpha * a * dt) and the cost thereof is quadratic (K * a^2 * dt), hence the name. There exists an exact, closed form solution for the fully continuous version of this game. References: [Perrin & al. 2019](https://arxiv.org/abs/2007.03458).
-🟢                                                                      | Mean Field Game: predator prey                                                                 | n/a     | n/a            | n/a          | n/a
+🟢                                                                      | Mean Field Game: predator prey                                                                 | n/a     | n/a            | n/a          | References: [Scaling up Mean Field Games with Online Mirror Descent](https://arxiv.org/abs/2103.00623), [Scalable Deep Reinforcement Learning Algorithms for Mean Field Games](https://arxiv.org/abs/2203.11973), [Learning in Mean Field Games: A Survey](https://arxiv.org/abs/2205.12944).
 🟢                                                                      | Mean Field Game: routing                                                                       | n/a     | ❌              | ✅            | Representative player chooses at each node where they go. They has an origin, a destination and a departure time and chooses their route to minimize their travel time. Time spent on each link is a function of the distribution of players on the link when the player reaches the link. References: [Cabannes et. al. '21, Solving N-player dynamic routing games with congestion: a mean field approach](https://arxiv.org/pdf/2110.11943.pdf).
 🔶                                                                      | [Morpion Solitaire (4D)](https://en.wikipedia.org/wiki/Join_five)                              | 1       | ✅              | ✅            | A single player game where player aims to maximize lines drawn on a grid, under certain limitations.
 🟢                                                                      | Negotiation                                                                                    | 2       | ❌              | ❌            | Agents with different utilities must negotiate an allocation of resources. References: [Lewis et al. '17](https://arxiv.org/abs/1706.05125). [Cao et al. '18](https://arxiv.org/abs/1804.03980).