This project implements a Reinforcement Learning (RL) based engine to dynamically adjust game difficulty. The goal is to provide an optimal and engaging experience for players by adapting the challenge level based on their performance and preferences.
Traditional game difficulty settings are often static, leading to frustration for new players or boredom for experienced ones. This engine leverages the power of Reinforcement Learning to create an adaptive difficulty system. By observing player actions and outcomes, the RL agent learns to modify game parameters (e.g., enemy health, spawn rates, item drops) in real-time, aiming to keep the player in a "flow state" – challenged but not overwhelmed.
- Dynamic Difficulty Adjustment: Adapts game parameters based on player performance.
- Reinforcement Learning Core: Utilizes RL algorithms (e.g., Q-learning, DQN) to learn optimal difficulty adjustments.
- Configurable Game Parameters: Easily define which game variables the engine can control.
- Player Performance Tracking: Monitors key metrics like win/loss ratio, time to complete levels, damage taken, etc.
- Modular Design: Allows for easy integration into existing game engines.
- Logging and Visualization: Provides tools to monitor the RL agent's learning process and difficulty changes.
The engine is composed of several key components:
- Game Interface: A module that allows the RL engine to interact with the game. This includes reading player state, game state, and applying difficulty changes.
- State Representation: Defines how the game state and player performance are translated into an observable state for the RL agent.
- Action Space: Defines the set of possible difficulty adjustments the RL agent can make.
- Reward Function: A crucial component that defines what constitutes a "good" or "bad" difficulty adjustment. This is typically based on player engagement, performance, and desired challenge level.