Deep Reinforcement Learning Course with Tensorflow

Description

Deep Reinforcement Learning with TensorFlow: Build Agents That Learn to Play and Solve

This course takes you from RL basics to deep learning-powered agents using TensorFlow and OpenAI Gym. You’ll build systems that master games, control robots, and optimize decisions—just like the agents developed at DeepMind and OpenAI.

What You’ll Build

• A Deep Q-Network (DQN) that plays CartPole and Breakout
• An Asynchronous Advantage Actor-Critic (A3C) for parallelized learning
• A Policy Gradient agent for continuous control tasks
• A custom Gym environment for your own problem

Why TensorFlow for RL?

• Production-ready—deploy models to mobile, web, or cloud
• Keras integration—build networks quickly with high-level APIs
• TensorBoard—visualize training curves, policies, and value functions
• Industry standard—used by Google, Uber, and many Fortune 500 companies

Key Concepts Covered

• Markov Decision Processes and Bellman equations
• Q-learning, SARSA, and Monte Carlo methods
• Deep Q-Networks with experience replay and target networks
• Actor-critic methods and policy gradients
• Exploration vs. exploitation strategies

Who Is This For?

• Python developers familiar with TensorFlow basics
• ML engineers expanding into sequential decision-making
• Students working on AI capstone projects
• Game developers adding intelligent NPCs

Your RL Journey Starts Here

You’ll graduate with a portfolio of working agents—and the deep understanding to adapt them to any domain.

Stop watching AI. Start building it. Enroll now.