The Deep Reinforcement Learning with TensorFlow course is designed to equip learners with the skills to build intelligent, self-learning systems that make optimal decisions through interaction with complex environments. This program covers foundational RL concepts such as reward functions, value estimation, and policy optimization, along with advanced algorithms including DQN, A3C, PPO, and Deep Deterministic Policy Gradients. Using TensorFlow, you will learn to design, train, and evaluate deep neural networks that power real-world RL applications in robotics, gaming, autonomous systems, finance, and recommendation engines. By the end of the course, you will be able to implement end-to-end RL pipelines, optimize performance, and deploy scalable models in practical scenarios.
Module 1: Introduction to Reinforcement Learning RL basics: agent, environment, states, actions, rewards Markov Decision Processes (MDPs) Exploration vs exploitation Real-world applications of RL.
Module 2: Python & TensorFlow Foundations for RL TensorFlow/Keras basics Neural network building blocks Data pipelines & model training workflow Setting up RL environments.
Module 3: Value-Based Methods Q-Learning Deep Q-Networks (DQN) Experience replay & target networks Implementing DQN from scratch in TensorFlow.
Module 4: Policy-Based Methods Stochastic policies Policy gradients REINFORCE algorithm Implementing policy gradient models.
Module 5: Actor–Critic Methods Advantage Actor-Critic (A2C/A3C) Entropy regularization Implementing Actor–Critic networks in TensorFlow.
Module 6: Advanced RL Algorithms Proximal Policy Optimization (PPO) Trust region methods Deep Deterministic Policy Gradient (DDPG) Soft Actor-Critic (SAC) fundamentals.
Module 7: Working With RL Environments OpenAI Gym / Gymnasium Unity ML-Agents Creating custom RL environments Sim-to-real challenges.
Module 8: Optimization & Training Strategies Reward shaping Hyperparameter tuning Debugging RL agents Improving sample efficiency.
Module 9: Deploying Deep RL Models Saving & loading agents TensorFlow Serving Deployment on cloud platforms Integrating RL with real-world systems.
Module 10: Capstone Projects & Case Studies End-to-end RL project (robotics, gaming, trading, etc.) Performance evaluation Best practices & model documentation Presentation of results.
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