Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

CodeSignal

Navigating RL Challenges: Strategies and Future Directions

via CodeSignal

Go to class Write review

Overview

The final course explores advanced techniques to enhance our RL systems. We'll implement random goal positions, hazardous environments with mines, and reward shaping, concluding with an exploration of cutting-edge RL developments that point toward future applications.

Syllabus

  • Unit 1: Implementing Random Goals in Grid World Environments
    • Random Goals in Grid World
    • Random Goal Positioning in GridWorld
    • Random Goal Selection in Grid World
    • Refine Random Goal Selection
    • Customizing Goal Distance in Grid World
  • Unit 2: Reward Shaping for Faster Learning in Reinforcement Learning
    • Fine-tune Reward Shaping Parameters
    • Fix the Distance Initialization Bug
    • Fix Reward Calculation Bug
    • Reward Shaping in Grid World
    • Tunable Reward Shaping in Grid World
  • Unit 3: Navigating Environmental Hazards in Reinforcement Learning
    • Mine Placement in GridWorld Environment
    • Random Mine Placement in GridWorld
    • Refine Agent's Reward System
    • Handling Mine Collisions in GridWorld
  • Unit 4: Designing Effective State Representations in Reinforcement Learning
    • Enhance State Representation Skills
    • Compass Direction Implementation in Grid World
    • Mine Detection for Safer Navigation
    • Edge Detection for Grid Navigation
    • Enhance Agent's Environmental Awareness
  • Unit 5: Exploring the Future of Reinforcement Learning
    • Reinforcement Learning Future Quiz

Reviews

Start your review of Navigating RL Challenges: Strategies and Future Directions

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.