Understand
Real Time Strategy (RTS) games require macro strategies as well as micro strategies to obtain satisfactory performance since it has large state space, action space, and hidden information.
- This paper presents a novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games.
- The novelty of this work are: (1) proposing a hierarchical framework, where agents execute macro strategies by imitation learning and carry out micromanipulations through reinforcement learning, (2) developing a simple self-learning method to get better sample efficiency for training, and (3) designing a dense reward function for multi-agent cooperation in the absence of game engine or Application Programming Interface (API).
- Finally, various experiments have been performed to validate the superior performance of the proposed method over other state-of-the-art reinforcement learning algorithms.