Deep Reinforcement Learning: Combining Neural Networks and Decision Making

Module 1: Introduction to Reinforcement Learning
✔️ Key concepts: Agents, environments, states, actions, rewards
✔️ Differences between supervised, unsupervised, and reinforcement learning
✔️ Real-world applications of reinforcement learning
✔️ Hands-on: Setting up a reinforcement learning environment with Python (OpenAI Gym)

Module 2: Markov Decision Processes (MDPs)
✔️ Markov Decision Process fundamentals
✔️ Bellman Equations
✔️ Value iteration and policy iteration
✔️ Hands-on: Solving MDP problems in Python

Module 3: Q-Learning and Deep Q-Networks (DQNs)
✔️ Introduction to Q-learning
✔️ Exploration vs exploitation dilemma
✔️ Deep Q-Networks: Using neural networks for Q-value approximation
✔️ Hands-on: Implementing Q-learning and a Deep Q-Network with TensorFlow/PyTorch

Module 4: Policy Gradient Methods
✔️ Policy gradients vs value-based methods
✔️ REINFORCE algorithm
✔️ Actor-Critic methods (A2C, A3C)
✔️ Hands-on: Implementing policy gradient algorithms for continuous action spaces

Module 5: Advanced Reinforcement Learning Techniques
✔️ Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO)
✔️ Hands-on: Implementing PPO using TensorFlow/PyTorch

Module 6: Deep Reinforcement Learning for Robotics
✔️ Applications in robotics and control systems
✔️ Case studies: RL in autonomous systems
✔️ Hands-on: Simulating a robotics task using reinforcement learning

Module 7: Model Evaluation and Optimization
✔️ Evaluating RL models: Reward structures, convergence
✔️ Hyperparameter tuning in reinforcement learning
✔️ Hands-on: Optimizing an RL model for a specific task

Module 8: Capstone Project: Building an End-to-End RL Agent
✔️ Problem formulation and environment setup
✔️ Model selection and training
✔️ Hands-on: Creating and deploying a fully trained RL agent