# Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups

arXiv: https://arxiv.org/abs/1406.0486 · HTML: https://mlsanity.com/paper/1406.0486/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2014.

## Built on by (most cited first, 11 of 11)

- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation](https://mlsanity.com/paper/2301.09709/) (2023)
- [The Role of Lookahead and Approximate Policy Evaluation in Reinforcement Learning with Linear Value Function Approximation](https://mlsanity.com/paper/2109.13419/) (2021)
- [Proof Number Based Monte-Carlo Tree Search](https://mlsanity.com/paper/2303.09449/) (2023)
- [Combining Monte-Carlo Tree Search with Proof-Number Search](https://mlsanity.com/paper/2206.03965/) (2022)
- [Two-Fidelity Best-Action Identification for Stochastic Minimax Tree](https://mlsanity.com/paper/2606.01708/) (2026)
- [Dual Monte Carlo Tree Search](https://mlsanity.com/paper/2103.11517/) (2021)
- [LBCIM: Loyalty Based Competitive Influence Maximization with epsilon-greedy MCTS strategy](https://mlsanity.com/paper/2303.01850/) (2023)
- [Generalized Proof-Number Monte-Carlo Tree Search](https://mlsanity.com/paper/2506.13249/) (2025)
- [Completeness of Unbounded Best-First Minimax and Descent Minimax](https://mlsanity.com/paper/2603.24572/) (2026)
- [varepsilon-Good Action Identification in Fixed-Budget Monte Carlo Tree Search](https://mlsanity.com/paper/2605.11324/) (2026)

## Abstract (arXiv)

Monte Carlo Tree Search (MCTS) has improved the performance of game engines in domains such as Go, Hex, and general game playing. MCTS has been shown to outperform classic alpha-beta search in games where good heuristic evaluations are difficult to obtain. In recent years, combining ideas from traditional minimax search in MCTS has been shown to be advantageous in some domains, such as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new way to use heuristic evaluations to guide the MCTS search by storing the two sources of information, estimated win rates and heuristic evaluations, separately. Rather than using the heuristic evaluations to replace the playouts, our technique backs them up implicitly during the MCTS simulations. These minimax values are then used to guide future simulations. We show that using implicit minimax backups leads to stronger play performance in Kalah, Breakthrough, and Lines of Action.