Master Study AI

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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