Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)

Deep Reinforcement Learning: Unlocking the Power of AI for Sequential Decision-Making Problems! 🚀 Are you ready to unleash the full potential of artificial intelligence on sequential decision-making problems? Look no further than "Foundations of Deep Reinforcement Learning"! 📚 This comprehensive guide combines both theory and implementation, providing a unique and powerful toolkit for solving complex problems in robotics, games, and beyond.

🤖 With this book, you'll learn how to: ☑️ Understand each key aspect of a deep RL problem ☑️ Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) ☑️ Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) ☑️ Understand how algorithms can be parallelized synchronously and asynchronously ☑️ Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work ☑️ Explore algorithm benchmark results with tuned hyperparameters ☑️ Understand how deep RL environments are designed Get ready to unlock the full potential of AI on sequential decision-making problems! 🔓 Order now and start your journey into the exciting world of deep reinforcement learning! 🚀