Comparing Diverse Planning Strategies with Continuous Monte Carlo Tree Search Applied to Hybrid Gene Regulatory Networks

Real-world applications of artificial intelligence often require the decision-maker to choose between multiple optimal solutions at hand before making a final decision. Since there is no guarantee that an increase in the budget or several independent executions will yield different solutions, classic mechanisms are not suitable for identifying multiple solutions. In the context of sequential decision-making problems, Monte-Carlo Tree Search (MCTS) is a state-of-the-art online planning algorithm. It is responsible for the improvement of many computer games but also for real-world problems involving continuous action spaces. MCTS has recently been successfully applied to the diverse planning problem in the discrete setting. In this work, we propose different diverse MCTS planners (DP-MCTS) to be relevant in the continuous setting. The solution to a diverse planning problem is a Pareto set between diversity and quality of plans. Therefore, we suggest considering a multi-objective setting in which the vectorial reward integrates the diversity measure as an additional objective. In addition, we propose two types of inhibition strategies disregarding the optimal plans to enforce the exploration of the search space during the tree construction. The three different contributions are assessed independently against a diverse multi-armed bandit policy, and the comparison is held on a real-world biological problem involving continuous action and state spaces.