PACHI: State of the Art Open Source Go Program
By Petr Baudis, J. Gailly. 2011
82 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Clinical Psychology, Economics and Econometrics, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Pieter Abbeel
- Yoshua Bengio
- Mohit Bansal
- Ruslan Salakhutdinov
- Chelsea Finn
- Quoc V. Le
- Jianfeng Gao
In Clinical Psychology
In Economics and Econometrics
- Yuandong Tian
- Yan Zhu
- David Silver
- Aja Huang
- Ilya Sutskever
- Chris J. Maddison
- Christopher Clark
- Amos J. Storkey
In Reinforcement Learning
- Sergey Levine
- Pieter Abbeel
- Chelsea Finn
- Shimon Whiteson
- Yuandong Tian
- Shixiang Shane Gu
- Ofir Nachum
- Joelle Pineau
Citations in the corpus
- OpenAI Gym (2016, background): “• Board games : currently, we have included the game of Go on 9x9 and 19x19 boards, where the Pachi engine [13] serves as an opponent.”
- Move Evaluation in Go Using Deep Convolutional Neural Networks (2014, background): “…of state-of-the-art Monte-Carlo search programs (such as Pachi) that are given 10,000 rollouts per move (i.e., programs that combine handcrafted or shallow prior knowledge with a search that simulates two million positions), and the first strong Monte-Carlo search program MoGo with 100,000…”
- Training Deep Convolutional Neural Networks to Play Go (2015, background): “In this section we provide a brief overview of Go, previous work, and the motivation for our deep learning based approach.”
- Better Computer Go Player with Neural Network and Long-term Prediction (2015, background): “Recurrent Neural Network is also tried but gives worse performance.”
- Neurohex: A Deep Q-learning Hex Agent (2016, unknown): “By comparison, AlphaGo tested their policy network against the strong Go program Pachi[5] and achieved an 85% win-rate.”