Reinforcement Learning Guide with TensorFlow

Welcome to the TensorFlow Reinforcement Learning (RL) documentation! 🤖🔍
This guide provides an overview of how to implement reinforcement learning algorithms using TensorFlow, including code examples and best practices.

Key Concepts

• Agent: The learner and decision-maker. 🧠
• Environment: The world where the agent interacts. 🌍
• Reward: Feedback signal to guide the agent's actions. 🎯
• Policy: Strategy that the agent uses to choose actions. 📊

Getting Started

1. Install TensorFlow

   pip install tensorflow
   

2. Choose an RL Algorithm

   • Deep Q-Network (DQN) 🎮
   • Policy Gradient (PG) 📈
   • Actor-Critic (AC) 🤝

3. Implement a Simple Example
   Check out the TensorFlow RL tutorial for step-by-step guidance. 📘

Resources

• TensorFlow RL GitHub Repository ⚡
• Advanced RL Techniques 🚀
• RL Theory Fundamentals 🧪