Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Maxim Lapan Deep Reinforcement Learning Hands-On Reviews Summary

Ratings Breakdown

Rated 4.4 by 172 people

Pros from Reviews

  • Ideal theory and practice mix
  • Clear and detailed explanations
  • Good practical examples
  • Comprehensive coverage of topics
  • Logical structure and progression
  • Helpful for beginners
  • Insightful ideas and tips
  • Good for hands-on learning

Cons from Reviews

  • Code often incompatible
  • Troubleshooting left to reader
  • Requires prior Python knowledge
  • Some concepts not explained
  • High resolution visuals lacking

Notable Features

Hands-on coding
Latest RL techniques
Updated for PyTorch
Multi-agent environments
Practical applications
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