On various games, TD($\lambda$)-n-tuple is found to be superior to other generic agents like MCTS.
In recent years, state-of-the-art game-playing agents often involve policies that are trained in self-playing processes where Monte Carlo tree search (MCTS) algorithms and trained policies iteratively improve each other.
This is due to the inherent difficulties of modern fighting games, including vast action spaces, real-time constraints, and performance generalizations required for various opponents.
Recent progress in reinforcement learning (RL) using self-game-play has shown remarkable performance on several board games (e. g., Chess and Go) as well as video games (e. g., Atari games and Dota2).
There has been a recent explosion in the capabilities of game-playing artificial intelligence.
In the past few years, deep reinforcement learning has been proven to solve problems which have complex states like video games or board games.
While reinforcement learning (RL) has been applied to turn-based board games for many years, more complex games involving decision-making in real-time are beginning to receive more attention.