Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors (2501.00078v1)

Abstract: AI has enabled agents to master complex video games, from first-person shooters like Counter-Strike to real-time strategy games such as StarCraft II and racing games like Gran Turismo. While these achievements are notable, applying these AI methods in commercial video game production remains challenging due to computational constraints. In commercial scenarios, the majority of computational resources are allocated to 3D rendering, leaving limited capacity for AI methods, which often demand high computational power, particularly those relying on pixel-based sensors. Moreover, the gaming industry prioritizes creating human-like behavior in AI agents to enhance player experience, unlike academic models that focus on maximizing game performance. This paper introduces a novel methodology for training neural networks via imitation learning to play a complex, commercial-standard, VALORANT-like 2v2 tactical shooter game, requiring only modest CPU hardware during inference. Our approach leverages an innovative, pixel-free perception architecture using a small set of ray-cast sensors, which capture essential spatial information efficiently. These sensors allow AI to perform competently without the computational overhead of traditional methods. Models are trained to mimic human behavior using supervised learning on human trajectory data, resulting in realistic and engaging AI agents. Human evaluation tests confirm that our AI agents provide human-like gameplay experiences while operating efficiently under computational constraints. This offers a significant advancement in AI model development for tactical shooter games and possibly other genres.

• The paper introduces a compute-efficient method using imitation learning and ray-cast sensors to develop human-like AI agents in tactical shooter games.
• The model, with 14.9 million parameters, achieves an average inference time of 9.59 ms per decision on standard gaming hardware.
• Evaluation via Jensen-Shannon divergence and human studies confirms that the bots closely mimic human strategic behavior, enhancing game realism.

Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors

The paper focuses on advancing AI in the field of tactical shooter games, particularly aiming to develop AI agents that exhibit human-like behavior with minimal computational overhead. By addressing the balance between performance and realism, it represents a significant effort to bridge the gap between academic AI models and their practical deployment in the gaming industry, a notoriously resource-constrained environment.

The authors introduce a novel method for training neural networks through imitation learning to play a VALORANT-like 2v2 tactical shooter game. The key achievement is that the AI agents require limited computational resources during inference, which is critical for real-time applications on consumer-grade hardware. This approach hinges on a new pixel-free perception architecture using ray-cast sensors that capture spatial information without the hefty computational demands typical of pixel-based sensors.

Methodology and Implementation

The AI agents are trained utilizing imitation learning methods on human gameplay data. The core of their methodology employs supervised learning to ingest and learn from human trajectory data, effectively mimicking human behavior. This involves processing data on movement, action sequences, and strategic decisions typically demonstrated by proficient players.

The sensor architecture, pivotal to the AI's performance, operates via a small set of ray-cast sensors instead of pixel-based inputs. These sensors offer a concise yet sufficient representation of the spatial environment, significantly reducing computational costs without sacrificing the quality of gameplay realism.

Experimental Results and Evaluation

The models undergo comprehensive evaluation involving several key metrics: CPU inference time, distributional similarity of gameplay characteristics, spatial similarity via heatmaps, and a human evaluation paper. The innovative sensor system allows the best-performing model, Model D—with approximately 14.9 million parameters—to achieve an average inference time of 9.59 milliseconds per decision on conventional gaming hardware. This demonstrates the practical deployability of the approach within the stringent constraints of commercial video games.

Crucially, the evaluation via Jensen-Shannon divergence and heatmap analysis confirms that the trained bots achieve a high degree of behavioral similarity to humans, particularly in strategic decision-making scenarios such as bomb planting and defusal, as well as in exhibiting movement patterns akin to human players. Furthermore, a human paper reinforces the perceived realism, where nearly a third of the participants misidentified bot behaviors as human.

Implications and Future Directions

The paper signifies a major progression in creating AI agents that can operate efficiently within the limited computational budgets typical of real-world gaming applications. Such advancements indicate a potential for broader application across various genres beyond tactical shooters.

In future developments, incorporating elements from reinforcement learning (RL) could enhance agent robustness and adaptability, potentially exploring uncharted strategy combinations that mimic not only observed human behavior but also an expanded set of possible actions. Investigating advanced architectural approaches like behavior transformers—recently shown to improve the handling of sequence modeling—could further enhance the agents' capabilities to generalize across different game situations and scenarios.

Overall, this research represents an important stride in efficiently aligning AI capabilities with industry requirements, providing a model that others developing AI systems in resource-constrained environments might emulate and build upon.