A Heterogeneous Multi-Agent Intelligent Player
For a Real-Time Strategy Game
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A Real-time Strategy (RTS) game is an adversarial game where the participants position and maneuver units and structures under their control to secure areas of the map and/or win by destroying their opponents' units and structures. It is possible to train new units and build new structures during the course of a game with limited amount of resources present in the setting. As opposed to comparable turn-based adversarial games such as chess and go, opponents do not wait for each others' moves so the game progresses naturally with units interacting each other in real time. As a result of the rea...
A Real-time Strategy (RTS) game is an adversarial game where the participants position and maneuver units and structures under their control to secure areas of the map and/or win by destroying their opponents' units and structures. It is possible to train new units and build new structures during the course of a game with limited amount of resources present in the setting. As opposed to comparable turn-based adversarial games such as chess and go, opponents do not wait for each others' moves so the game progresses naturally with units interacting each other in real time. As a result of the real-time nature of the game, the complexity of the game dramatically increases. In recent years, there has been an increasing interest in RTS games in the artificial intelligence community, especially from the perspective of reinforcement learning, to create an agent to play the RTS game, due to the problem's large state-action space. Modeling and implementing an efficient learning agent that is able to cope with the large state space is a difficult task.
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