A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.

C++ & Java implementations of 6 algorithm problems — tromino tiling, knight's tour, Tower of Hanoi (4-peg), knight swap, target shooting, and lattice coverage — using Divide & Conquer, Greedy, BFS, ...