Game Expansion and Reinforcement Learning

Understanding RL in Gaming Context

Core RL Concepts

Reinforcement Learning enables our AI agent to learn through trial and error by:

• Taking actions in the game environment

• Receiving rewards or penalties based on outcomes

• Adjusting its strategy based on accumulated experience

• Building a policy that maps game states to optimal actions

Current Implementation with DOOM

In DOOM, our RL agent learns by:

• Processing visual input from the game screen

• Understanding spatial relationships and enemy positions

• Managing resources (health, ammo, armor)

• Developing combat strategies and movement patterns

Potential Game Expansions

Counter-Strike Series

Counter-Strike games offer excellent opportunities for RL training:

CS:GO

• Complex team-based strategies

• Economic resource management

• Precise aim mechanics

• Map awareness and positioning

• Utility usage (grenades, flashbangs)

Counter-Strike 2

• Updated engine with improved visibility

• Enhanced movement mechanics

• More sophisticated physics interactions

• Advanced sound propagation learning

Call of Duty: Modern Warfare

Modern Warfare presents unique challenges:

• Faster-paced combat scenarios

• Multiple game modes for varied training

• Advanced movement mechanics

• Weapon customization optimization

• Complex map traversal

Learning Objectives

Different games can focus on different skills:

• Tactical decision making

• Aim precision

• Movement optimization

• Resource management

• Team coordination

Implementation Challenges

Each game expansion requires:

• Custom reward structures

• Appropriate state representations

• Action space mapping

• Performance metrics adaptation

• Environmental interaction protocols