Reinforcement Learning in Gaming Full Syllabus

Module 1: Introduction to Reinforcement Learning (RL)

• What is Reinforcement Learning?
• How RL differs from Supervised & Unsupervised Learning
• Core concepts: Agent, Environment, Reward, State, Action, Policy
• The reward hypothesis in gaming
• Real-world RL in gaming: AlphaGo, OpenAI Five, DeepMind’s Atari agents

Module 2: The RL Framework (Markov Decision Processes)

• Markov Decision Process (MDP) Basics
• States, Actions, Transitions, Rewards
• Discount Factor (γ) & Future Rewards
• Episodic vs Continuous tasks
• Modeling games as MDPs

Module 3: Classic RL Algorithms

• Q-Learning
• SARSA (State-Action-Reward-State-Action)
• ε-Greedy Policy & Exploration vs Exploitation
• Gridworld & Maze Games with Classic Q-learning
• Implementing simple agents using Python & Gym

Module 4: OpenAI Gym & RL Environments

• Introduction to OpenAI Gym
• Installing and using Gym
• Understanding Gym Environments (CartPole, FrozenLake, Atari)
• Creating custom game environments for RL
• Visualizing agent performance

Module 5: Deep Reinforcement Learning (DRL)

• Why Deep Learning in RL?
• Deep Q-Networks (DQN)
• Neural Network as Q-function approximator
• Experience Replay & Target Networks
• Training an agent to play Pong, Breakout, etc.

Module 6: Policy-Based Methods

• Introduction to Policy Gradient Algorithms
• REINFORCE Algorithm
• Advantage Actor-Critic (A2C)
• Proximal Policy Optimization (PPO)
• Implementing policy-based agents using PyTorch or TensorFlow

Module 7: Advanced Game AI Techniques

• Multi-agent reinforcement learning
• Hierarchical RL for complex games
• Reward shaping & curriculum learning
• Sim2Real: Training in simulation, deploying in real game
• Combining rules + RL (Hybrid systems)

Module 8: Integrating RL into Game Engines

• Unity ML-Agents Toolkit
• Training in Unity with PPO / DQN
• RL in Unreal Engine with AIRL / DeepMind Lab
• C# vs Python interfaces
• Using RL for NPC movement, combat, and pathfinding

Module 9: RL for Open-World & Multiplayer Games

• Partial observability (POMDPs)
• Persistent world learning
• Dealing with non-stationary environments
• Cooperative & Competitive Multi-agent systems (e.g., hide-and-seek bots)
• Cross-agent communication strategies

Module 10: Evaluation, Tuning & Safety

• Metrics for agent performance
• Hyperparameter tuning strategies
• Debugging & interpreting agent behavior
• Avoiding overfitting & reward hacking
• Ethical concerns & agent safety

Module 11: Cutting-Edge Topics

• DeepMind’s MuZero & Model-based RL
• AlphaStar (StarCraft 2 AI)
• RLHF (Reinforcement Learning from Human Feedback)
• Self-play & Curriculum-based progression
• Using Transformers in RL

Capstone Projects

1. Train an RL agent to play a custom 2D platformer
2. Build a Unity ML-agent that learns to avoid enemies and collect coins
3. Implement a multi-agent hide-and-seek game using PPO
4. Train an NPC to learn dialogue-based strategy (RL + NLP)
5. Build a DQN bot for a turn-based RPG battle system