On Robustness of CMAB Algorithms: Experimental Approach

In online planning with a team of cooperative agents, a straightforward model for decision making which actions the agents should execute can be represented as the problem of Combinatorial Multi-Armed Bandit. Similarly to the most prominent approaches for online planning with polynomial number of possible actions, state-of-the-art algorithms for online planning with exponential number of actions are based on Monte-Carlo sampling. However, without a proper selection of the appropriate subset of actions these techniques cannot be used. The most recent algorithms tackling this problem utilize an assumption of linearity with respect to the combinations of the actions. In this paper, we experimentally analyze robustness of two state-of-the-art algorithms NMC and LSI for online planning with combinatorial actions in various setups of Real-Time and Turn-Taking Strategy games.