Unlock the power of Reinforcement Learning to make machines that learn by doing.

Reinforcement Learning Basics is your hands-on guide to building intelligent systems that learn from experience, adapt to dynamic environments, and make decisions autonomously. From robotics and gaming to real-world decision-making, this book equips beginners and intermediate developers with the skills to implement RL algorithms effectively using practical examples.

Inside, you'll learn how to:

• Understand core RL concepts: agents, environments, states, actions, rewards, and policies.

• Implement classic RL algorithms such as Q-Learning, SARSA, and Monte Carlo methods.

• Explore deep reinforcement learning techniques with neural networks for complex environments.

• Apply policy gradient methods and actor-critic models to optimize decision-making.

• Train autonomous agents in simulated environments using Python and popular RL libraries.

• Utilize reward shaping and exploration strategies to accelerate learning.

• Integrate RL into robotics applications for navigation, manipulation, and adaptive control.

• Develop gaming AI agents that adapt, compete, and improve over time.

• Build decision-making systems for finance, logistics, and operations using RL frameworks.

• Evaluate and fine-tune RL models with metrics to ensure optimal performance.

Packed with hands-on projects, step-by-step tutorials, and real-world examples, this book gives you the foundation to create autonomous, learning systems that can tackle dynamic, uncertain environments confidently.

• Aspiring AI developers wanting to explore reinforcement learning

• Robotics enthusiasts seeking autonomous control solutions

• Game developers looking to build intelligent, adaptive AI agents

• Students, researchers, and professionals interested in applied RL for decision-making

Learn the fundamentals of Reinforcement Learning and start building intelligent systems that act, adapt, and improve on their own.