Chapter 1: Introduction to Reinforcement Learning
- Lesson 1: What is Reinforcement Learning?
- Lesson 2: Key Concepts: Agent, Environment, State, Action, and Reward
- Lesson 3: Reinforcement Learning vs Supervised and Unsupervised Learning
- Lesson 4: Applications of Reinforcement Learning
- Lesson 5: Overview of the Course
Chapter 2: Markov Decision Processes (MDPs)
- Lesson 1: Fundamentals of MDPs
- Lesson 2: States, Actions, and Transition Probabilities
- Lesson 3: Reward Functions and Return
- Lesson 4: Policy and Value Functions
- Lesson 5: Solving MDPs
Chapter 3: Dynamic Programming for RL
- Lesson 1: Bellman Equations
- Lesson 2: Policy Evaluation
- Lesson 3: Policy Iteration
- Lesson 4: Value Iteration
- Lesson 5: Limitations of Dynamic Programming
Chapter 4: Monte Carlo Methods
- Lesson 1: Fundamentals of Monte Carlo Learning
- Lesson 2: First-Visit and Every-Visit Monte Carlo Methods
- Lesson 3: Estimation of Value Functions
- Lesson 4: Exploring-Starts and Importance Sampling
- Lesson 5: Applications of Monte Carlo Methods
Chapter 5: Temporal-Difference Learning
- Lesson 1: Fundamentals of TD Learning
- Lesson 2: TD(0) and Eligibility Traces
- Lesson 3: SARSA: On-Policy Learning
- Lesson 4: Q-Learning: Off-Policy Learning
- Lesson 5: Comparing TD, Monte Carlo, and DP
Chapter 6: Exploration vs Exploitation
- Lesson 1: The Exploration-Exploitation Trade-off
- Lesson 2: ε-Greedy Strategies
- Lesson 3: Upper Confidence Bound (UCB)
- Lesson 4: Softmax Action Selection
- Lesson 5: Balancing Exploration and Exploitation
Chapter 7: Function Approximation
- Lesson 1: Limitations of Tabular Methods
- Lesson 2: Linear Function Approximation
- Lesson 3: Nonlinear Function Approximation
- Lesson 4: Features and Basis Functions
- Lesson 5: Challenges with Approximation
Chapter 8: Policy-Based Methods
- Lesson 1: Policy Gradient Theorem
- Lesson 2: REINFORCE Algorithm
- Lesson 3: Variance Reduction Techniques
- Lesson 4: Advantage Function and Actor-Critic Methods
- Lesson 5: Policy Optimization Challenges
Chapter 9: Multi-Armed Bandits
- Lesson 1: Basics of Multi-Armed Bandits
- Lesson 2: Greedy and ε-Greedy Policies
- Lesson 3: UCB in Bandit Problems
- Lesson 4: Thompson Sampling
- Lesson 5: Bandits in RL
Chapter 10: Case Studies and Practical Applications
- Lesson 1: RL in Games (Chess, Go, etc.)
- Lesson 2: RL in Robotics
- Lesson 3: RL for Recommendation Systems
- Lesson 4: RL for Autonomous Driving
- Lesson 5: Building a Simple RL Project
Chapter 1: Deep Reinforcement Learning (DRL)
- Lesson 1: Introduction to Deep RL
- Lesson 2: Role of Neural Networks in RL
- Lesson 3: DQN (Deep Q-Networks)
- Lesson 4: Double DQN and Dueling DQN
- Lesson 5: Prioritized Experience Replay
Chapter 2: Policy Optimization Methods
- Lesson 1: Trust Region Policy Optimization (TRPO)
- Lesson 2: Proximal Policy Optimization (PPO)
- Lesson 3: Soft Actor-Critic (SAC)
- Lesson 4: Deterministic Policy Gradients (DDPG)
- Lesson 5: Advanced Variants and Challenges
Chapter 3: Adversarial Deep Reinforcement Learning
- Lesson 1: Basics of Adversarial Learning in RL
- Lesson 2: Generative Adversarial Imitation Learning (GAIL)
- Lesson 3: Adversarial Attacks in RL
- Lesson 4: Robust RL against Adversaries
- Lesson 5: Case Studies
Chapter 4: Multi-Objective Reinforcement Learning (MORL)
- Lesson 1: Introduction to MORL
- Lesson 2: Scalarization Techniques
- Lesson 3: Pareto Front in MORL
- Lesson 4: MORL in Real-World Applications
- Lesson 5: Challenges in MORL
Chapter 5: Fuzzy Reinforcement Learning
- Lesson 1: Introduction to Fuzzy Systems in RL
- Lesson 2: Fuzzy Q-Learning
- Lesson 3: Combining Fuzzy Logic and Neural Networks in RL
- Lesson 4: Applications of Fuzzy RL
- Lesson 5: Advanced Fuzzy RL Techniques
Chapter 6: Inverse Reinforcement Learning (IRL)
- Lesson 1: Basics of IRL
- Lesson 2: Apprenticeship Learning via IRL
- Lesson 3: Maximum Entropy IRL
- Lesson 4: Deep IRL
- Lesson 5: Applications of IRL
Chapter 7: Reinforcement Learning from Human Feedback (RLHF)
- Lesson 1: Human-in-the-Loop Reinforcement Learning
- Lesson 2: Reward Modeling with Human Feedback
- Lesson 3: Applications in Large Language Models (LLMs)
- Lesson 4: Challenges and Future Directions
- Lesson 5: Ethical Considerations
Chapter 8: Fine-Tuning Vision-Language Models (VLMs)
- Lesson 1: Overview of VLMs and Reinforcement Learning
- Lesson 2: Fine-Tuning Techniques for VLMs
- Lesson 3: Applications in RL with VLMs
- Lesson 4: Case Studies of VLM Fine-Tuning
- Lesson 5: Limitations and Future Directions
Chapter 9: OpenAI Gym Library
- Lesson 1: Introduction to OpenAI Gym
- Lesson 2: Environment Creation and Interaction
- Lesson 3: Customizing Environments
- Lesson 4: Case Studies with OpenAI Gym
- Lesson 5: Advanced Features
Chapter 10: RLlib Library
- Lesson 1: Overview of RLlib
- Lesson 2: Using RLlib for Distributed RL
- Lesson 3: Algorithms Supported in RLlib
- Lesson 4: Customizing RLlib Models
- Lesson 5: Case Studies
Chapter 11: Coach Library
- Lesson 1: Introduction to Intel Coach
- Lesson 2: Supported Algorithms and Features
- Lesson 3: Implementing RL Solutions with Coach
- Lesson 4: Customization and Extensions
- Lesson 5: Applications
Chapter 12: Keras-RL Library
- Lesson 1: Introduction to Keras-RL
- Lesson 2: Building RL Agents with Keras-RL
- Lesson 3: Customizing Models and Policies
- Lesson 4: Case Studies with Keras-RL
- Lesson 5: Advanced Techniques
Chapter 13: Real-World Applications and Case Studies
- Lesson 1: RL in Financial Markets
- Lesson 2: RL for Healthcare Optimization
- Lesson 3: RL for Energy Management
- Lesson 4: RL in Education and Training
- Lesson 5: Future Directions in RL
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