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Course Outline
1. Introduction to Deep Reinforcement Learning
- What is Reinforcement Learning?
- Difference between Supervised, Unsupervised, and Reinforcement Learning
- Applications of DRL in 2025 (robotics, healthcare, finance, logistics)
- Understanding the agent-environment interaction loop
2. Reinforcement Learning Fundamentals
- Markov Decision Processes (MDP)
- State, Action, Reward, Policy, and Value functions
- Exploration vs. Exploitation trade-off
- Monte Carlo methods and Temporal-Difference (TD) learning
3. Implementing Basic RL Algorithms
- Tabular methods: Dynamic Programming, Policy Evaluation, and Iteration
- Q-Learning and SARSA
- Epsilon-greedy exploration and decaying strategies
- Implementing RL environments with OpenAI Gymnasium
4. Transition to Deep Reinforcement Learning
- Limitations of tabular methods
- Using neural networks for function approximation
- Deep Q-Network (DQN) architecture and workflow
- Experience replay and target networks
5. Advanced DRL Algorithms
- Double DQN, Dueling DQN, and Prioritized Experience Replay
- Policy Gradient Methods: REINFORCE algorithm
- Actor-Critic architectures (A2C, A3C)
- Proximal Policy Optimization (PPO)
- Soft Actor-Critic (SAC)
6. Working with Continuous Action Spaces
- Challenges in continuous control
- Using DDPG (Deep Deterministic Policy Gradient)
- Twin Delayed DDPG (TD3)
7. Practical Tools and Frameworks
- Using Stable-Baselines3 and Ray RLlib
- Logging and monitoring with TensorBoard
- Hyperparameter tuning for DRL models
8. Reward Engineering and Environment Design
- Reward shaping and penalty balancing
- Sim-to-real transfer learning concepts
- Custom environment creation in Gymnasium
9. Partially Observable Environments and Generalization
- Handling incomplete state information (POMDPs)
- Memory-based approaches using LSTMs and RNNs
- Improving agent robustness and generalization
10. Game Theory and Multi-Agent Reinforcement Learning
- Introduction to multi-agent environments
- Cooperation vs. competition
- Applications in adversarial training and strategy optimization
11. Case Studies and Real-World Applications
- Autonomous driving simulations
- Dynamic pricing and financial trading strategies
- Robotics and industrial automation
12. Troubleshooting and Optimization
- Diagnosing unstable training
- Managing reward sparsity and overfitting
- Scaling DRL models on GPUs and distributed systems
13. Summary and Next Steps
- Recap of DRL architecture and key algorithms
- Industry trends and research directions (e.g., RLHF, hybrid models)
- Further resources and reading materials
Requirements
- Proficiency in Python programming
- Understanding of Calculus and Linear Algebra
- Basic knowledge of Probability and Statistics
- Experience building machine learning models using Python and NumPy or TensorFlow/PyTorch
Audience
- Developers interested in AI and intelligent systems
- Data Scientists exploring reinforcement learning frameworks
- Machine Learning Engineers working with autonomous systems
21 Hours
Testimonials (5)
Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.
Rick Johnson - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Very flexible.
Frank Ueltzhoffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.
Gudrun Bickelq
Course - Introduction to the use of neural networks
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
