Policy Gradients Beat Game Theory in Hidden-Info Games

New research from MIT and other universities is rewriting the playbook on how to win complex games with hidden information—like poker or bidding wars—by proving that older, general-purpose AI methods actually outperform traditional game theory algorithms. Instead of just creating a new algorithm, the team built a powerful new benchmark to measure how easily a player can be beaten by a perfect opponent, revealing that neural networks trained with policy gradient methods achieve lower exploitability scores and win head-to-head. Tested across games like Tic-Tac-Toe, Hex, and Liar’s Dice—even at massive state scales—they’ve made their tool freely available for anyone to use, opening doors to better strategy in everything from business deals to military planning. Support the show:Get a discount at https://solipillow.com/discount/dnn. Advertise on DNN:[email protected] This is an automated, high-level news summary based on public reporting.Report issues to [email protected]. View sources & latest updates:https://sources.thednn.ai/33bbeaab7e57609c