How Reinforcement Learning Works in Game Theory
Game Theory is a mathematical framework designed around the interactions between multiple decision-makers who try to maximize their individual payoff. This mathematical approach to social interactions has many applications in the world of business and finance but also has a growing relationship with Artificial Intelligence (AI), more specifically Reinforcement Learning (RL).
Reinforcement Learning is a subfield of AI that recreates an environment for the AI agent to learn and adapt to. During the training process, the agent uses Deep Learning (DL) algorithms to evaluate its performance, creating more optimized routines for future use.
By combining the mathematical framework with Reinforcement Learning techniques, developers are creating new, more sophisticated AI models that are capable of performing a large range of tasks including economic forecasting, resource allocation, and supply management.
Understanding Game Theory
Game Theory is a branch of mathematics that studies strategic interactions, specifically scenarios where every possible decision affects the other players. The study assumes that every participant is a rational player, meaning they all understand and accept their goal of maximizing their payoff.
In every scenario, players will analyze strategic behaviors to identify potential strategies, initiating actions on their turns. An equilibrium can be encountered if all players reach a point where no remaining strategies can help maximize their payoffs.
Some common examples of games include:
Zero-Sum: In this game, every payoff results in an equal loss for the other player. The sum of gains and losses always equals zero.
Cooperative & Non-Cooperative: Binding agreements are either allowed or not allowed in these forms of games, leading players towards forming coalitions or working independently.
Symmetric & Asymmetric Games: Players can either have equal payoffs or payoffs that slightly differ to alter strategies and goals.
Simultaneous & Sequential Games: Some games can take place at the same time, or in turns, causing players to analyze the order of events.
Perfect and Imperfect Information Games: These games will make information about other players more scarce or abundant.
What is Reinforcement Learning?
Reinforcement Learning is a type of Machine Learning that places an Agent, which is an AI model, inside an environment where it must learn how to interact with its surroundings. The agent’s goal is to create policies that help the agent operate within the environment, maximizing rewards.
Reinforcement Learning is crucial within the field of AI because, unlike most Supervised Learning models which look for hidden patterns, RL algorithms focus on optimizing their operations using the information found within their environment.
RL is especially helpful in real-world scenarios where uncertainty plays a larger factor in the decision-making process. Unlike pre-determined training environments, real-world situations contain many unknown variables that are difficult to predict; giving RL a unique advantage in the realm of AI.
How Reinforcement Learning Works
Reinforcement Learning is a process with multiple steps focusing on developing the agent’s decision-making process. It begins with the exploration and exploitation phases that involve processing the environment and beginning to make decisions based on its surroundings.
Once the environment has been analyzed, the agent will make policies that help it maximize rewards, and one of the most common algorithms in RL to do this is called Q-Learning. With this algorithm, the agent will maintain a table of q-values for each state-action pair.
These q-values will signal to the agent the expected reward of action, giving it more information to analyze for decision-making. A simplified version of the process looks like this:
Initialize the Q-Table with initial values
Observe the current state of the environment
Choose an action
Perform the action
Update the Q-values
Repeat
The Application of Reinforcement Learning in Game Theory
Game Theory and Reinforcement Theory can intersect in many ways once multiple agents are deployed within the same environment. In these scenarios, agents can either work together or independently depending on the game being played and their specific objectives.
Achieving Nash Equilibrium, when all players in a game have reached a state where they can no longer improve their optimization, can be highly beneficial in real-world scenarios like self-automation on roads and streets where self-driving cars can be viewed as agents that must all work together to optimize transportation efficiency.
The Future of Reinforcement Learning in Game Theory
The future of Reinforcement Learning and Game Theory is bright and only growing as AI technology continues to develop at an exponential rate. With more Multi-Agent Reinforcement Learning Models (MARLs), developers can create more scalable and efficient AI models for larger, more complex tasks.
In time, industries of all types like gaming, finance, and cybersecurity, are expected to grow from the impact of MARLs and their ability to optimize exploitable value in any given system whether it be an economic model or traffic infrastructure.
