: thoroughly-tested. In many cases,
we verified against known values and/or reproduced results from papers.
~: implemented but lightly tested.
X: known problems; please see github issues.
| Algorithms | Category | Reference | Status |
|---|---|---|---|
| Information Set Monte Carlo Tree Search (IS-MCTS) | Search | Cowley et al. '12 | ~ |
| Minimax (and Alpha-Beta) Search | Search | Wikipedia1, Wikipedia2, Knuth and Moore '75 | |
| Monte Carlo Tree Search | Search | Wikipedia, UCT paper, Coulom '06, Cowling et al. survey | |
| Lemke-Howson (via nashpy) | Opt. | Wikipedia, Shoham & Leyton-Brown '09 | |
| ADIDAS | Opt. | Gemp et al '22 | ~ |
| Sequence-form linear programming | Opt. | Koller, Megiddo, and von Stengel '94, Shoham & Leyton-Brown '09 |
|
| Stackelberg equilibrium solver | Opt. | Conitzer & Sandholm '06 | ~ |
| Magnetic Mirror Descent (MMD) with dilated entropy | Opt. | Sokota et al. '22 | ~ |
| Counterfactual Regret Minimization (CFR) | Tabular | Zinkevich et al '08, Neller & Lanctot '13 | |
| CFR against a best responder (CFR-BR) | Tabular | Johanson et al '12 | |
| Exploitability / Best response | Tabular | Shoham & Leyton-Brown '09 | |
| External sampling Monte Carlo CFR | Tabular | Lanctot et al. '09, Lanctot '13 | |
| Fixed Strategy Iteration CFR (FSICFR) | Tabular | Neller & Hnath '11 | ~ |
| Mean-field Ficticious Play for MFG | Tabular | Perrin et. al. '20 | ~ |
| Online Mirror Descent for MFG | Tabular | Perolat et. al. '21 | ~ |
| Munchausen Online Mirror Descent for MFG | Tabular | Lauriere et. al. '22 | ~ |
| Fixed Point for MFG | Tabular | Huang et. al. '06 | ~ |
| Boltzmann Policy Iteration for MFG | Tabular | Lauriere et. al. '22 | ~ |
| Outcome sampling Monte Carlo CFR | Tabular | Lanctot et al. '09, Lanctot '13 | |
| Policy Iteration | Tabular | Sutton & Barto '18 | |
| Q-learning | Tabular | Sutton & Barto '18 | |
| Regret Matching | Tabular | Hart & Mas-Colell '00 | |
| Restricted Nash Response (RNR) | Tabular | Johanson et al '08 | ~ |
| SARSA | Tabular | Sutton & Barto '18 | |
| Value Iteration | Tabular | Sutton & Barto '18 | |
| Advantage Actor-Critic (A2C) | RL | Mnih et al. '16 | |
| Deep Q-networks (DQN) | RL | Mnih et al. '15 | |
| Ephemeral Value Adjustments (EVA) | RL | Hansen et al. '18 | ~ |
| Proximal Policy Optimization (PPO) | RL | Schulman et al. '18 | ~ |
| AlphaZero (C++/LibTorch) | MARL | Silver et al. '18 | |
| AlphaZero (Python/TF) | MARL | Silver et al. '18 | |
| Correlated Q-Learning | MARL | Greenwald & Hall '03 | ~ |
| Asymmetric Q-Learning | MARL | Kononen '04 | ~ |
| Deep CFR | MARL | Brown et al. '18 | |
| Exploitability Descent (ED) | MARL | Lockhart et al. '19 | |
| (Extensive-form) Fictitious Play (XFP) | MARL | Heinrich, Lanctot, & Silver '15 | |
| Nash Q-Learning | MARL | Hu & Wellman '03 | ~ |
| Neural Fictitious Self-Play (NFSP) | MARL | Heinrich & Silver '16 | |
| Neural Replicator Dynamics (NeuRD) | MARL | Omidshafiei, Hennes, Morrill, et al. '19 | X |
| Regret Policy Gradients (RPG, RMPG) | MARL | Srinivasan, Lanctot, et al. '18 | |
| Policy-Space Response Oracles (PSRO) | MARL | Lanctot et al. '17 | |
| Q-based ("all-actions") Policy Gradient (QPG) | MARL | Srinivasan, Lanctot, et al. '18 | |
| Regularized Nash Dynamics (R-NaD) | MARL | Perolat, De Vylder, et al. '22 | |
| Regression CFR (RCFR) | MARL | Waugh et al. '15, Morrill '16 | |
| Rectified Nash Response (PSRO_rn) | MARL | Balduzzi et al. '19 | ~ |
| Win-or-Learn-Fast Policy-Hill Climbing (WoLF-PHC) | MARL | Bowling & Veloso '02 | ~ |
| α-Rank | Eval. / Viz. | Omidhsafiei et al. '19, arXiv | |
| Nash Averaging | Eval. / Viz. | Balduzzi et al. '18 | ~ |
| Replicator / Evolutionary Dynamics | Eval. / Viz. | Hofbaeur & Sigmund '98, Sandholm '10 | |