How can I design a reinforcement learning model for a game with multiple complex actions taken at a time? I have a steady hex-map and turn-based wargame featuring WWII carrier battles.
On a given turn, a player may choose to perform a large number of actions. Actions can be of many different types, and some actions may be performed independently of each other while others have dependencies. For example, a player may decide to move one or two naval units, then assign a mission to an air unit or not, then adjust some battle parameters or not, and then reorganize a naval task force or not.
 Usually, boardgames allow players to perform only one action each turn (e.g. go or chess) or a few very similar actions (backgammon).
Here the player may select

Several actions
The actions are of different nature
Each action may have parameters that the player must set (e.g. strength, payload, destination)

How could I approach this problem with reinforcement learning? How would I specify a model or train it effectively to play such a game?
Here is a screenshot of the game.

Here's another.
The master's thesis Action space representation in combinatorial multi-armed bandits (2015) seems to provide an answer to my question.
Several algorithms can be used

Naive Monte-Carlo Sampling (NMC)
Linear Side Information (LSI)
Monte Carlo Tree Search with Hierarchical Expansion (MCTS-HE)
MCTS with Dimensional Expansion

The idea is to divide and conquer.
Here's a screenshot of the HE algorithm from section 4.2.