Reward Shaping for Improved Learning in Real-time Strategy Game Play
This addresses the challenge of slow learning in games with infrequent rewards, though it is incremental as it applies known reward shaping techniques to a specific domain.
The study tackled the problem of sparse rewards in reinforcement learning for a real-time strategy capture-the-flag game by designing reward shaping functions, resulting in significantly improved player performance and reduced training times.
We investigate the effect of reward shaping in improving the performance of reinforcement learning in the context of the real-time strategy, capture-the-flag game. The game is characterized by sparse rewards that are associated with infrequently occurring events such as grabbing or capturing the flag, or tagging the opposing player. We show that appropriately designed reward shaping functions applied to different game events can significantly improve the player's performance and training times of the player's learning algorithm. We have validated our reward shaping functions within a simulated environment for playing a marine capture-the-flag game between two players. Our experimental results demonstrate that reward shaping can be used as an effective means to understand the importance of different sub-tasks during game-play towards winning the game, to encode a secondary objective functions such as energy efficiency into a player's game-playing behavior, and, to improve learning generalizable policies that can perform well against different skill levels of the opponent.