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Board Games

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/michael-beukman/robocupgym

공식 코드

RobocupGym: A challenging continuous control benchmark in Robocup

https://github.com/mit-futuretech/thecomputationallimitsofdeeplearning

공식 코드

The Computational Limits of Deep Learning

https://github.com/mwpainter/thts-plus-plus

공식 코드

Monte Carlo Tree Search with Boltzmann Exploration

https://github.com/omikad/probs

공식 코드

Playing Board Games with the Predict Results of Beam Search Algorithm

https://github.com/opendilab/LightZero

공식 코드

LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios

https://github.com/orenneumann/alphazero_zipfs_law

공식 코드

AlphaZero Neural Scaling and Zipf's Law: a Tale of Board Games and Power Laws

https://github.com/pikaju/muzero-g

공식 코드

Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision

https://github.com/pytorch/ELF

공식 코드

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

https://github.com/richoux/pobo

공식 코드

Injecting Combinatorial Optimization into MCTS: Application to the Board Game boop

https://github.com/rl-interpretation/understandingRL

공식 코드

Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution

https://github.com/rl-language/4hammer

공식 코드

4Hammer: a board-game reinforcement learning environment for the hour long time frame

https://github.com/rl-language/rlc

공식 코드

4Hammer: a board-game reinforcement learning environment for the hour long time frame

https://github.com/rlglab/minizero

공식 코드

MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games

https://github.com/rlglab/online-fine-tuning-solver

공식 코드

Game Solving with Online Fine-Tuning

https://github.com/rmbpo-eval/rmbpo-eval

공식 코드

Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model

https://github.com/rockmanray/rzone

공식 코드

A Novel Approach to Solving Goal-Achieving Problems for Board Games

https://github.com/sagebei/impartial-games-a-chanllenge-for-reinforcement-learning

공식 코드

Impartial Games: A Challenge for Reinforcement Learning

https://github.com/shtoshni/learning-chess-blindfolded

공식 코드

Chess as a Testbed for Language Model State Tracking

https://github.com/shtoshni92/learning-chess-blindfolded

공식 코드

Chess as a Testbed for Language Model State Tracking

https://github.com/sidharth0094/game-of-ur-reinforcement-learning

공식 코드

Solving Royal Game of Ur Using Reinforcement Learning

https://github.com/wh1992v/ggp-rl

공식 코드

Assessing the Potential of Classical Q-learning in General Game Playing

https://github.com/wty-yy/katacr

공식 코드

Playing Non-Embedded Card-Based Games with Reinforcement Learning

https://github.com/yannikkellerde/gnn_hex

공식 코드

From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?

https://github.com/zayenz/cp-2019-nmbr9

공식 코드

Nmbr9 as a Constraint Programming Challenge

https://github.com/zayenz/cp-mod-ref-2019-patchwork

공식 코드

State Representation and Polyomino Placement for the Game Patchwork

https://github.com/zerosumeval/zerosumeval

공식 코드

ZeroSumEval: An Extensible Framework For Scaling LLM Evaluation with Inter-Model Competition

https://github.com/030helios/Kata2Surakarta

Accelerating Self-Play Learning in Go

https://github.com/AIRLegend/ChessRL

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/BertilBraun/ChessBot

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/Branbados/rlcard

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/CI-labo-OPU/FML_Competition2020

Better Computer Go Player with Neural Network and Long-term Prediction

https://github.com/ComarPers922/AI-based-3D-Game-Simulator

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/DHDev0/Muzero

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/FrankPortman/stannis

Monte Carlo Q-learning for General Game Playing

https://github.com/John-Yu/leelaApplication

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/JuanCCS/muzero-jc

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.