5 citations · 5 across the 2 of their papers we have counts for
4 papers
Dual Monte Carlo Tree Search
Prashank Kadam, Ruiyang Xu, Karl Lieberherr
AlphaZero, using a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS), has successfully trained reinforcement learning agents in a tabula-rasa way. The neural M…
Solving QSAT problems with neural MCTS
Ruiyang Xu, Karl Lieberherr
Recent achievements from AlphaZero using self-play has shown remarkable performance on several board games. It is plausible to think that self-play, starting from zero knowledge, c…
First-Order Problem Solving through Neural MCTS based Reinforcement Learning
Ruiyang Xu, Prashank Kadam, Karl Lieberherr
The formal semantics of an interpreted first-order logic (FOL) statement can be given in Tarskian Semantics or a basically equivalent Game Semantics. The latter maps the statement…
Learning Self-Game-Play Agents for Combinatorial Optimization Problems
Ruiyang Xu, Karl Lieberherr
Recent progress in reinforcement learning (RL) using self-game-play has shown remarkable performance on several board games (e.g., Chess and Go) as well as video games (e.g., Atari…