1
AI-Based Dice Roller Using Reinforcement
Learning
Your Name Department of Computer Science
Your University
City, Country
email@domain.com
Abstract—This project proposes the development of an AI- III. E XPECTED O UTCOMES
based dice roller using reinforcement learning (RL). The system
will simulate a simplified dice game, such as Pig, and train
The expected outcomes of this project include:
an RL agent to make optimal decisions on whether to roll 1) A functional dice game environment with well-defined
the dice or stop to maximize the score. The project aims to rules and mechanics.
demonstrate the application of RL in decision-making under 2) A trained RL agent capable of playing the game opti-
uncertainty and provide a foundation for more complex game AI
systems. The proposed methodology includes designing the game
mally.
environment, implementing Q-Learning or Deep Q-Networks 3) Insights into the training process, including hyperparam-
(DQN), and evaluating the agent’s performance. The expected eter tuning and performance evaluation.
outcomes include a functional AI-based dice roller and insights 4) A user interface for testing and visualizing the agent’s
into RL training strategies. decisions.
Index Terms—Reinforcement Learning, Dice Game, Q- 5) A foundation for extending the project to more complex
Learning, Deep Q-Networks, Artificial Intelligence games or multi-agent systems.
I. I NTRODUCTION IV. M ETHODOLOGY
Dice games have been a popular form of entertainment for The proposed methodology for this project is as follows:
centuries, with simple rules and elements of chance. Modern
advancements in artificial intelligence (AI) and reinforcement A. Game Design
learning (RL) provide an opportunity to create intelligent A simplified dice game, such as Pig, will be implemented.
systems capable of playing such games optimally. This project The game rules include:
focuses on developing an AI-based dice roller that uses RL to • Rolling a six-sided die.
make decisions in a simplified dice game, such as Pig. The • Losing the turn if a 1 is rolled.
RL agent will learn to maximize its score by deciding when • Adding the die value to the turn score if 2–6 is rolled.
to roll the dice and when to stop, balancing risk and reward. • Deciding whether to roll again or stop and bank the turn
The primary motivation for this project is to explore the score.
application of RL in decision-making under uncertainty. By • Winning the game by reaching a target score (e.g., 100).
simulating a dice game, we can study how an AI agent
learns to navigate stochastic environments and make optimal
decisions. The project also serves as a practical introduction B. Reinforcement Learning Setup
to RL concepts, such as state-action spaces, reward systems, The RL framework will include:
and exploration vs. exploitation. • State Space: Current turn score, total score, and last dice
roll value.
II. O BJECTIVES • Action Space: Roll the dice or stop.
• Reward System: Positive rewards for points gained,
The main objectives of this project are as follows:
negative rewards for losing turn points, and terminal
1) Design and implement a simplified dice game environ- rewards for winning the game.
ment with clear rules and mechanics.
2) Develop an RL agent using Q-Learning or Deep Q-
C. Training the Agent
Networks (DQN) to play the game.
3) Train the RL agent to make optimal decisions and The RL agent will be trained using Q-Learning or DQN.
maximize its score. The training process will involve:
4) Evaluate the agent’s performance against baseline strate- • Simulating multiple episodes of the game.
gies or human players. • Updating the Q-table or neural network based on rewards.
5) Provide a user-friendly interface for testing and visual- • Balancing exploration and exploitation using an epsilon-
izing the agent’s decisions. greedy strategy.
2
D. Testing and Evaluation
The trained agent will be tested against baseline strategies
or human players. Performance metrics, such as win rate and
average score, will be used to evaluate the agent’s effective-
ness.
E. Enhancements
Optional enhancements include:
• Adding visualization using Matplotlib or Pygame.
• Extending the game rules or implementing multi-agent
support.
• Developing a web or mobile application for the AI-based
dice roller.
V. R EFERENCES
R EFERENCES
[1] Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An
Introduction. MIT Press.
[2] Mnih, V., et al. (2015). Human-level control through deep reinforcement
learning. Nature, 518(7540), 529-533.
[3] Neller, T. W., & Presser, C. G. M. (2004). Optimal Play of the Dice
Game Pig. The UMAP Journal, 25(1), 25-47.
[4] Brockman, G., et al. (2016). OpenAI Gym. arXiv preprint
arXiv:1606.01540.
