Self-play RL in a takeover auction model shows optimal due diligence is modest and finite, decreasing with cost and competition, while simple general methods outperform specialized ones in large intractable games.
Combining Deep Rein- forcement Learning and Search for Imperfect-Information Games, November 2020
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Basic dataset creation, embedding learning, and evaluation tasks on Kuhn and Leduc Poker demonstrate that useful behavioral representations appear in the learned embeddings.
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How Much Due Diligence Before You Bid? Learning in Intractable Takeover Auctions
Self-play RL in a takeover auction model shows optimal due diligence is modest and finite, decreasing with cost and competition, while simple general methods outperform specialized ones in large intractable games.