Reward Shaping for Improved Learning in Real-time Strategy Game Play
John Kliem Prithviraj Dasgupta
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.