Self-play Monte-Carlo tree search in computer poker

By Johannes Heinrich, David Silver. 2014

15 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Kaiqing Zhang
2. Zhuoran Yang
3. Marc Lanctot
4. Johannes Heinrich
5. David Silver
6. Tamer Basar
7. Yuxi Li
8. Sergey Levine

Citations in the corpus

• Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games (2015, result): “We hope to compare practical performance against other baseline algorithms such as PIMC [28], MMCTS [1], IIMC [10], and Smooth UCT [18].”
• Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019, methodology): “This framework was later adopted by [271, 25, 30, 31] to incorporate other single RL methods such as deep Q-network [10] and Monte-Carlo tree search [53, 52, 272].”
• Smooth UCT Search in Computer Poker (2015, methodology): “We submitted SmooCT, an agent trained with an early version of the Smooth UCT algorithm [Heinrich and Silver, 2014], to the 2014 ACPC, where it achieved three silver medals in the LHE competitions.”