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Engine
Research
.gitignore
LoCM.jar
Player.nim
README.md
Runner.nim
Tester.nim

README.md

Build

# Debug build
nim c Player.nim
nim c Runner.nim
nim c Tester.nim

# Release build
nim c -d:release Player.nim
nim c -d:release Runner.nim
nim c -d:release Tester.nim

CLI

./Player --draft="" --player="" --seed=N --state="" --time=N
./Runner --p1-draft="" --p1-player="" --p1-state="" --p1-time=N --p2-draft="" --p2-player="" --p2-state="" --p2-time=N --games=N --verbose=false
./Tester --referee="" --plain=false --player1="" --player2="" --games=N --threads=N --replays=false

Run

nim c -d:release Runner.nim
./Runner \
  --p1-draft=Icebox \
  --p1-player=DFS \
  --p1-time=100 \
  --p2-draft=ClosetAI \
  --p2-player=FMC \
  --p2-time=150 \
  --games=16 \
  --verbose=false

Run (CG referee)

nim c -d:release Player.nim
nim c -d:release Tester.nim
./Tester \
  --referee="java -jar LoCM.jar" \
  --player1="./Player --draft=Icebox --player=DFS --time=100" \
  --player2="./Player --draft=ClosetAI --player=FMC --time=150" \
  --games=16 \
  --threads=4

Roadmap

Draft evaluations:

Player algorithms:

  • Bridge Burning Monte Carlo Tree Search (--player=BBMCTS, source)
  • DFS (--player=DFS, source)
  • Flat Monte Carlo (--player=FMC, source)
  • Greedy (--player=Greedy, source)
  • Monte Carlo Tree Search (--player=MCTS, source)
  • Monte Carlo Tree Search - lookahead (--player=MCTS0, source)
  • Monte Carlo Tree Search + pruning (--player=MCTSP, source)
  • Noop (--player=Noop, source)
  • Random (--player=Random, source)

State evaluations:

  • Simple (--state=Simple, source)
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