Leduc Hold'em
PulseAugur coverage of Leduc Hold'em — every cluster mentioning Leduc Hold'em across labs, papers, and developer communities, ranked by signal.
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New LUGL framework enables gradient-boosted trees for RL game-playing
Researchers have developed a new framework called LUGL (Local Updates, Global Learning) that allows non-incremental learners, such as gradient-boosted trees (GBTs), to be effectively used in reinforcement learning (RL) …
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New LUGL framework enables gradient-boosted trees for reinforcement learning games
Researchers have developed a new framework called LUGL (Local Updates, Global Learning) that allows non-incremental learners, such as gradient-boosted trees (GBTs), to be effective in reinforcement learning (RL) setting…
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New CS-RNR method allows AI agents to certify their own exploits in games
Researchers have developed a new method called confidence-scheduled restricted responses (CS-RNR) for agents playing imperfect-information games. This technique allows agents to certify their own exploits, ensuring that…
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New study reveals key techniques for training strong lightweight game-playing AI agents
Researchers have developed a robust method for training lightweight agents in imperfect-information card games like Gin Rummy and Leduc Hold'em. By using a fixed, strong expert agent as a benchmark, they identified key …
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StratFormer AI learns to model and exploit opponents in imperfect-information games
Researchers have developed StratFormer, a novel transformer-based agent designed for imperfect-information games. This agent learns to both model and exploit opponent behaviors through a two-phase training curriculum. S…