Board Games
43 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Board Games
Libraries
Use these libraries to find Board Games models and implementations
Obstacle Tower
Cards Against Humanity
Latest papers
Solving Royal Game of Ur Using Reinforcement Learning
Reinforcement Learning has recently surfaced as a very powerful tool to solve complex problems in the domain of board games, wherein an agent is generally required to learn complex strategies and moves based on its own experiences and rewards received.
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes).
Impartial Games: A Challenge for Reinforcement Learning
sagebei/impartial-games-a-chanllenge-for-reinforcement-learning
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Intuitively, the difference between impartial games like Nim and partisan games like Chess and Go can be explained by the fact that if a small part of the board is covered for impartial games it is typically not possible to predict whether the position is won or lost as there is often zero correlation between the visible part of a partly blanked-out position and its correct evaluation.
Split Moves for Monte-Carlo Tree Search
These decisions can be viewed as separate moves, which is already a common practice in multi-action games for efficiency reasons.
AlphaDDA: Strategies for Adjusting the Playing Strength of a Fully Trained AlphaZero System to a Suitable Human Training Partner
We believe that the AlphaDDA approach for DDA can be used for any game AI system if the DNN can accurately estimate the value of the game state and we know a parameter controlling the skills of the AI system.
Planning in Stochastic Environments with a Learned Model
opendilab/LightZero
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ICLR 2022
However, previous instantiations of this approach were limited to the use of deterministic models.
AlphaZero-based Proof Cost Network to Aid Game Solving
We train a Proof Cost Network (PCN), where proof cost is a heuristic that estimates the amount of work required to solve problems.
Estimates for the Branching Factors of Atari Games
The branching factor of a game is the average number of new states reachable from a given state.
Playing Codenames with Language Graphs and Word Embeddings
Although board games and video games have been studied for decades in artificial intelligence research, challenging word games remain relatively unexplored.
Scaling Scaling Laws with Board Games
The largest experiments in machine learning now require resources far beyond the budget of all but a few institutions.
