Reinforcement Learning & Game AI
artificial-intelligence-ai.
🎓 Course Modules:
📦 Module 1: Introduction to Reinforcement Learning
What is reinforcement learning?
Key concepts: agents, environments, states, actions, rewards
Comparison with supervised and unsupervised learning
🎯 Module 2: The RL Framework
Markov Decision Processes (MDPs)
Reward functions and policy definitions
Exploration vs. exploitation dilemma
🧠 Module 3: Q-Learning
Tabular Q-learning basics
Epsilon-greedy strategies
Q-table implementation in Python
🔁 Module 4: Deep Q-Networks (DQN)
Neural networks for value function approximation
Experience replay and target networks
Implementing DQNs with TensorFlow or PyTorch
🎮 Module 5: OpenAI Gym & Game Environments
Setting up and navigating OpenAI Gym
Working with built-in environments: CartPole, MountainCar, Pong
Visualizing training performance
🧮 Module 6: Policy Gradient Methods
Introduction to policy optimization
REINFORCE algorithm
Stochastic policies and reward signals
🧠 Module 7: Actor-Critic & Advantage Methods
Combining value and policy-based methods
A2C and A3C algorithms
Stable learning and convergence techniques
🤖 Module 8: Multi-Agent Reinforcement Learning
Cooperative and competitive agents
Shared environments and communication
Strategy learning in multiplayer games
🧪 Module 9: Game AI Design Techniques
Rule-based vs. learning-based agents
AI for NPC behavior modeling
Reward shaping and curriculum learning
✅ Module 10: Final Capstone Project
Build a game-playing agent using DQN or A2C
Choose from projects: maze solver, Pong AI, or autonomous car in a simulated world
Project presentation and peer review
🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI
📚Shop our library of over one million titles and learn anytime
👩🏫 Learn with our expert tutors
