An Agent for EinStein Wurfelt Nicht! Using N-Tuple Networks

Abstract

This paper describes the implementation of an agent that plays EinStein wurfelt nicht!. The agent is based on the common Monte-Carlo tree search (MCTS) which is especially good at dealing with the randomness in a game. For the agent, this paper proposes to use n-tuple networks trained by Monte-Carlo learning. In the agent, the trained n-tuple network is used together with MCTS by the following three approaches: progressive bias, prior knowledge and c-greedy. The experimental results show that epsilon-greedy improved the playing strength the most, which obtained a win rate of 61.05% against the baseline agent. By combining all three approaches, the win rate increased a little to 62.25%. And the enhanced agent also won the first place in the EinStein wurfelt nicht! tournament in Computer Olympiad 2017.