Abstract
Learning rational behaviors in First-person-shooter (FPS) games is a challenging task for Reinforcement Learning (RL) with the primary difficulties of huge action space and insufficient exploration. To address this, we propose a hierarchical agent based on combined options with intrinsic rewards to drive exploration. Specifically, we present a hierarchical model that works in a manager-worker fashion over two levels of hierarchy. The high-level manager learns a policy over options, and the low-level workers, motivated by intrinsic reward, learn to execute the options. Performance is further improved with environmental signals appropriately harnessed. Extensive experiments demonstrate that our trained bot significantly outperforms the alternative RL-based models on FPS games requiring maze solving and combat skills, etc.
Notably, we achieved first place in VDAIC 2018 Track(1).
Keywords
- Machine Learning: Reinforcement Learning
- Heuristic Search and Game Playing: General Game Playing and General Video Game Playing
- Machine Learning Applications: Game Playing
- Planning and Scheduling: Markov Decisions Processes
