Reinforcement Learning Agent for a 2D Shooter Game

• URL: http://arxiv.org/abs/2509.15042v1
• Date: Thu, 18 Sep 2025 15:07:41 GMT
• Title: Reinforcement Learning Agent for a 2D Shooter Game
• Authors: Thomas Ackermann, Moritz Spang, Hamza A. A. Gardi,
• Abstract summary: Reinforcement learning agents in complex game environments often suffer from sparse rewards, training instability, and poor sample efficiency.<n>This paper presents a hybrid training approach that combines offline imitation learning with online reinforcement learning for a 2D shooter game agent.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning agents in complex game environments often suffer from sparse rewards, training instability, and poor sample efficiency. This paper presents a hybrid training approach that combines offline imitation learning with online reinforcement learning for a 2D shooter game agent. We implement a multi-head neural network with separate outputs for behavioral cloning and Q-learning, unified by shared feature extraction layers with attention mechanisms. Initial experiments using pure deep Q-Networks exhibited significant instability, with agents frequently reverting to poor policies despite occasional good performance. To address this, we developed a hybrid methodology that begins with behavioral cloning on demonstration data from rule-based agents, then transitions to reinforcement learning. Our hybrid approach achieves consistently above 70% win rate against rule-based opponents, substantially outperforming pure reinforcement learning methods which showed high variance and frequent performance degradation. The multi-head architecture enables effective knowledge transfer between learning modes while maintaining training stability. Results demonstrate that combining demonstration-based initialization with reinforcement learning optimization provides a robust solution for developing game AI agents in complex multi-agent environments where pure exploration proves insufficient.

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