The Reinforcement Learning (RL) Framework: Learning Through Interaction
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Course Modules:
Module 1: What is Reinforcement Learning?
How RL differs from supervised and unsupervised learning
Real-world examples: gaming, robotics, finance, and healthcare
Core goal: maximizing cumulative reward
Module 2: The RL Framework Components
Agent: the learner or decision-maker
Environment: the world the agent interacts with
State: the current situation of the agent
Action: choices the agent can make
Reward: feedback received after an action
Module 3: The Learning Loop
The observation-action-reward cycle
Exploration vs. exploitation trade-off
Episode-based learning and convergence
Module 4: Policies, Value Functions & Models
Policy: the agent's strategy
Value function: expected future reward
Model of the environment: optional use in model-based RL
Module 5: Tools and Libraries for RL
Introduction to OpenAI Gym
Overview of RL libraries: Stable Baselines, RLlib, TensorFlow Agents
Running your first simulation
Module 6: Capstone Project – Simulate the RL Framework
Choose a Gym environment (e.g., CartPole, MountainCar, FrozenLake)
Set up the agent, environment, and reward strategy
Visualize the learning process and submit your notebook or demo
Tools & Technologies Used:
Python
OpenAI Gym
NumPy, Matplotlib
Optional: TensorFlow, PyTorch, Stable-Baselines3
Target Audience:
Students of machine learning and AI
Developers exploring reinforcement learning
Game designers and robotics engineers
Anyone curious about how AI learns from experience
Global Learning Benefits:
Understand how interactive learning works in AI
Build simulations that reflect real-world decision-making
Lay the groundwork for advanced RL concepts like Q-learning and deep reinforcement learning
Gain hands-on experience with tools used in the industry
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