The factors of reinforcement learning in Gaming Theory

Machine learning (ML) improves with training, and reinforcement learning is one of the methods most commonly used in game theory. Other reinforcement learning applications include robotics and resource management. These innovative uses make reinforcement learning more relevant in other industries as more researchers explore its capabilities.

What Is Reinforcement Learning in Games?

When it comes to console and mobile game development, reinforcement learning projects are an ML training technique that seeks rewards. The algorithm learns over repetitive experiences where exemplary performance earns rewards and mistakes cause repercussions.

Game developers want the algorithm to undergo trial and error, eventually becoming intimately familiar with desirable and undesirable circumstances — which the developer assigned values for the algorithm to identify and associate. Ultimately, the algorithm will make mistakes less frequently as it desires and conceptualizes the value behind positive outcomes.

In game theory, AI wastes less time completing objectives and is more competent against competing players. The AI will only increase in efficiency if the rewards never cease. In gaming, several quintessential factors are operating these trial-and-error experiments.

The Policy-Driven Agent

Iterative behavior is the driving force behind reinforcement learning. The agent, or a machine learning game bot, thrives off a developer-assigned policy to perform functions. While the agent obtains rewards, it becomes more knowledgeable about its policy, increasing its competence.

Developers have algorithmic variations they use to experiment with reinforcement learning policy effectiveness:

• State-action-reward-state-action (SARSA): The developer informs the agent of the probabilities of success.
• Q-learning: This version has no policy, and the developer relies on the algorithm to lead itself through gaming environments.
• Deep q-learning: Developers can choose to combine neural networks with reinforcement learning. The gaming algorithm will have no policy, but it can use the data from the network about positive outcomes to make determinations.

Whatever policy is used, repeated actions allow the agent to become more familiar with its environment and how it interacts. Every iteration will deliver more informed decisions as the AI traverses the game’s landscape.

The Environmental Exploration

Reinforcement learning works well in game theory because it relies on the agent’s interactivity with the environment. The more the agent explores, the more it learns what will benefit and detract from the experience. Developers can also change the agent’s motivation, exploring the world more selfishly or collaboratively, for example. Without an exploratory atmosphere, reinforcement learning isn’t practical.

Game developers create mostly static environments. The ability for a domain to change is fixed within the code. Though gaming environments can shift, there are finite possibilities for how they can manipulate. There are enough unique environmental qualities and shifts to familiarize the agent with the world, but not so much it’s overwhelming.

Agents navigating infinitely variable environments will find it challenging to make stark determinations concerning positive and negative outcomes when outliers could throw data off-balance.

Unpredictable environmental stressors have been a primary reason reinforcement learning doesn’t apply to many sectors, though some have tried to use it in the financial industry when predicting stocks. Unfortunately, the dynamics are too varied, with too many outliers for the agent to become accurate and proficient.

The Time Investment

The other major factor in reinforcement learning in gaming theory is how much time the process takes. Since the agent must engage in countless repetitions of varying lengths of environmental engagement, informing the algorithm takes longer than other types of AI learning, such as supervised learning.

As the agent obtains more information about the game and how frequently they can access rewards for previously interacted stimuli, the computing power required to operate the sessions becomes more intensive. In addition to consuming more time to perform training, the system also requires more resources.

Testing takes even longer when developers introduce more variables or make changes to the algorithm. Suppose developers want to incorporate Markov’s Decision Process (MDP). In that case, it can lead the agent to the best-case scenario in the fixed gaming landscape without consideration of past experiences. Developers can include past decision-making data or not experiment with how the agent runs.

What’s Most Important in Reinforcement Learning for Games

The agent’s policy, environmental freedom and time are all game theory needs to execute expert reinforcement learning. These factors will inform future experimentation with reinforcement learning in other sectors, such as personal finance. Though it’s still in its early stages, there is great potential for this algorithmic model to create more nuanced AI.