Why Isn't Reinforcement Learning Widely Used for Game AI Agents?

Reinforcement learning (RL) has powered impressive achievements in game-playing AI, such as AlphaGo and OpenAI’s Dota 2 bot. Tools like Unity ML Agents and examples like Gran Turismo 7’s RL-trained AI racer show its potential. Yet, RL remains rare in complex games for NPCs or opponents. Why is this powerful technology not more widely adopted?

RL Solves a Different Problem Than Game AI Needs

Game AI is often about creating entertaining experiences, not just winning. As DMGregory explains, RL excels at optimizing for victory in unknown or complex problem spaces, often discovering surprising strategies. However, game developers typically want AI that:

• Plays to lose entertainingly: Opponents should challenge players but ultimately let them win in a fun way.
• Behaves predictably: Players enjoy manipulating predictable AI, and designers can fine-tune behaviors for consistent fun.
• Avoids exploits or weird behaviors: RL can produce unexpected or buggy-looking actions that break immersion.

Defining a reward function for “fun” is tricky, and RL’s emergent behaviors are hard to control, making it less ideal for crafting specific player experiences.

Players Prefer Human Opponents for True Challenges

For players seeking tough, adaptive opponents, multiplayer modes with human rivals are often preferred. Human opponents foster social connections—friends, rivals, or streaming audiences—that AI struggles to replicate. As DMGregory notes, RL could be useful for training modes to prepare players for competitive play, but the development cost is high compared to matchmaking systems that pair beginners together.

Personally I find games where the AI takes the place of a human opponent or partner are not as entertaining as playing with another person. Also the satisfaction of seeing someone downcast and humbled, when you beat them in a just ended game is not something AI can easily emulate.

Traditional AI Techniques Are Still Effective

Theraot highlights that game AI often relies on established methods like behavior trees, finite state machines, and pathfinding. These were once considered cutting-edge AI and remain effective because they are:

• Predictable and tunable: Easier for designers to control and debug.
• Flexible for changes: Unlike RL models, which require retraining after game updates, traditional methods adapt quickly.

These methods align better with the need for consistent, designer-driven AI behaviors.

Practical Challenges of RL in Games

Implementing RL in games faces several hurdles:

• Training time: Training RL models during gameplay is impractical. Game sessions are too short, and players interact with individual enemies briefly, as Theraot points out. Pre-trained models are more feasible but still require significant upfront work.
• Performance costs: Training or running complex RL models takes CPU resources, which games need for smooth rendering and physics.
• Debugging difficulty: RL models are hard to interpret or fix when they behave oddly, unlike traditional AI systems. Side note: Found out the hard way about this when I tried to use reinforcement learning for my 3D game.

An exception is Black & White by Lionhead Studios, which used a small neural network trained during gameplay for pet creature behavior. Players could reward or punish actions, shaping the creature’s behavior. However, such examples are rare due to the complexity of scaling this approach.

Other Uses of Machine Learning in Games

While RL for agents is uncommon, machine learning has found niches in games:

• Augmented reality: Facial and skeletal tracking use pre-trained models.
• Decision-making: Supreme Commander 2 used a pre-trained neural network for unit decisions.
• Graphics: Deep Learning Super Sampling (DLSS) enhances performance and visuals.
• Generative AI: Some indie games experiment with large language models for dialogue or gimmicks, but these often run on remote servers.

Future Potential for RL in Games

Emerging research, like the “Forward-Forward Algorithm” (2022), could make RL training faster and less resource-intensive, potentially enabling real-time learning for companion AI or dynamic difficulty systems. However, the core challenge of aligning RL with game design goals—entertaining, predictable, and tunable AI—remains.

Conclusion

Reinforcement learning’s limited adoption in games stems from a mismatch between its strengths (optimizing to win) and game AI’s goals (entertaining and predictable behavior). Traditional AI methods are more practical, and human opponents satisfy the need for challenging play. While RL has potential for specific use cases like training modes or companion AI, its high development cost, performance demands, and debugging challenges make it a tough fit for most games. As machine learning evolves, we may see more creative integrations, but for now, RL remains a niche tool in game development.

This post is inspired

by discussions on StackOverflow.