Abstract
Monte Carlo Tree Search (MCTS) has recently seen considerable success in playing certain types of games, most of which are discrete, fully observable zero-sum games. Consequently there is currently considerable interest within the research community in investigating what other games this algorithm might play well, and how it can be modified to achieve this. In this paper, we investigate the application of MCTS to simulated car racing, in particular
the open-source racing game TORCS. The presented approach is based on the development of an efficient forward model and the discretization of the action space. This combination allows the controller to effectively search the tree of potential future states.
Results show that it is indeed possible to implement a competent MCTS-based racing controller. The controller generalizes to most road tracks as long as a warm-up period is provided.
the open-source racing game TORCS. The presented approach is based on the development of an efficient forward model and the discretization of the action space. This combination allows the controller to effectively search the tree of potential future states.
Results show that it is indeed possible to implement a competent MCTS-based racing controller. The controller generalizes to most road tracks as long as a warm-up period is provided.
| Original language | English |
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| Title of host publication | Proceedings of the 10th International Conference on the Foundations of Digital Games (FDG 2015), June 22-25, 2015, Pacific Grove, CA, USA |
| Number of pages | 5 |
| Publisher | Association for Computing Machinery |
| Publication date | 2015 |
| ISBN (Electronic) | 978-0-9913982-4-9 |
| Publication status | Published - 2015 |