In this course, we introduce the basic Reinforcement Learning (RL) framework and explore how to build a simple environment from scratch. We discuss states, actions, and rewards, then outline how an environment class should be structured and implement it.
Overview
Syllabus
- Unit 1: Understanding the Foundations of Reinforcement Learning
- Grid World State Exploration
- Expanding the Grid World Adventure
- State Transitions in Grid World
- Debugging Agent Movement in Grid
- Reward Function Exploration in Grid World
- Unit 2: Building a Grid World Environment for Reinforcement Learning
- Building Your Grid World Environment
- Correcting the Goal State
- Resetting the Grid World Environment
- Custom Starting Position in Grid World
- Building Your Own Grid World
- Unit 3: Implementing Step and Render Methods in Grid World Environment
- Debugging Grid World Boundaries
- Reward and Termination in Grid World
- Visualize the Grid World Journey
- Building a Complete Grid World
- Unit 4: Interacting with Grid World: Implementing a Random Agent
- Random Agent in Grid World
- Debugging the Random Agent
- Smart Moves in Grid World
- Creating a Random Agent Function
- Guiding the Agent to Success
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