Abstract
The AlphaZero algorithm and its successor MuZero have revolutionised several competitive strategy games, including chess, Go, and shogi and video games like Atari, by learning to play these games better than any human and any specialised computer program. Aside from knowing the rules, AlphaZero had no prior knowledge of each game. This dramatically advanced progress on a long-standing AI challenge to create programs that can learn for themselves from first principles. Theoretically, there are well-known limits to the power of deep learning for strategy games like chess, Go, and shogi, as they are known to be NEXPTIME hard. Some papers have argued that the AlphaZero methodology has limitations and is unsuitable for general AI. However, none of these works has suggested any specific limits for any particular game. In this paper, we provide more powerful bottlenecks than previously suggested. We present the first concrete example of a game - namely the (children) game of nim - and other impartial games that seem to be a stumbling block for AlphaZero and similar reinforcement learning algorithms. We show experimentally that the bottlenecks apply to both the policy and value networks. Since solving nim can be done in linear time using logarithmic space i.e. has very low-complexity, our experimental results supersede known theoretical limits based on many games' PSPACE (and NEXPTIME) completeness. We show that nim can be learned on small boards, but when the board size increases, AlphaZero style algorithms rapidly fail to improve. We quantify the difficulties for various setups, parameter settings and computational resources. Our results might help expand the AlphaZero self-play paradigm by allowing it to use meta-actions during training and/or actual game play like applying abstract transformations, or reading and writing to an external memory.
Abstract (translated)
URL
https://arxiv.org/abs/2205.12787
