Search Results for author:
Found 8 papers, 1 papers with code
Transformer Based Planning in the Observation Space with Applications to Trick Taking Card Games
Traditional search algorithms have issues when applied to games of imperfect information where the number of possible underlying states and trajectories are very large.
Inference-Based Deterministic Messaging For Multi-Agent Communication
In this paper, we first study learning in matrix-based signaling games to empirically show that decentralized methods can converge to a suboptimal policy.
Bayes DistNet -- A Robust Neural Network for Algorithm Runtime Distribution Predictions
It can also quantify its uncertainty in its predictions, allowing for algorithm portfolio models to make better informed decisions about which algorithm to run on a particular instance.
Single-Agent Optimization Through Policy Iteration Using Monte-Carlo Tree Search
The combination of Monte-Carlo Tree Search (MCTS) and deep reinforcement learning is state-of-the-art in two-player perfect-information games.
Policy Based Inference in Trick-Taking Card Games
Trick-taking card games feature a large amount of private information that slowly gets revealed through a long sequence of actions.
Learning Policies from Human Data for Skat
In trick-taking card games like Bridge or Skat, large information sets and an inability to advance the simulation without fully determinizing the state make forward search problematic.
Improving Search with Supervised Learning in Trick-Based Card Games
In trick-taking card games, a two-step process of state sampling and evaluation is widely used to approximate move values.
Combining Strategic Learning and Tactical Search in Real-Time Strategy Games
The network is then used to select a script --- an abstract action --- to produce low level actions for all units.
