Modular Reinforcement Learning for Playing the Game of Tron
Mingi Jeon, Jay Lee, Sang‐Ki Ko · IEEE Access · 2022
Tron is a simultaneous move two-player game where a wall is created along the path where two agents move and the agent that crash with the wall first is defeated. Due to the fact that the same action may result in different outcomes (non-stationarity), it is difficult to utilize the basic approach of reinforcement learning. In this paper, we present a modular reinforcement learning approach to tackling the game of Tron by decomposing the game into two phases where the first phase is non-stationary and the second phase is stationary. We evaluate the performance of our algorithm by comparing with previous algorithms including the state-of-the-art algorithm for the game of Tron (called a1k0n) in different grid sizes.