Course Outline
1. Introduction to Deep Reinforcement Learning
- Defining Reinforcement Learning
- Distinguishing between Supervised, Unsupervised, and Reinforcement Learning
- DRL applications in 2025 across robotics, healthcare, finance, and logistics
- Understanding the loop of agent-environment interaction
2. Reinforcement Learning Fundamentals
- Markov Decision Processes (MDP)
- The trade-off between exploration and exploitation
- 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
- Implementing RL environments using OpenAI Gymnasium
4. Transition to Deep Reinforcement Learning
- Limitations of tabular methods
- Utilizing 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: The 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
- Utilizing Stable-Baselines3 and Ray RLlib
- Hyperparameter tuning for DRL models
8. Reward Engineering and Environment Design
- Reward shaping and balancing penalties
- Concepts of sim-to-real transfer learning
- Creating custom environments in Gymnasium
9. Partially Observable Environments and Generalization
- Managing incomplete state information (POMDPs)
- Memory-based approaches using LSTMs and RNNs
- Enhancing agent robustness and generalization
10. Game Theory and Multi-Agent Reinforcement Learning
- Introduction to multi-agent environments
- Balancing cooperation and competition
- Applications in adversarial training and strategy optimization
11. Case Studies and Real-World Applications
- Dynamic pricing and financial trading strategies
- Robotics and industrial automation
12. Troubleshooting and Optimization
- Diagnosing unstable training
- Addressing reward sparsity and overfitting
- Scaling DRL models across 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
- Solid proficiency in Python programming
- A fundamental understanding of Probability and Statistics
- Practical experience in developing machine learning models using Python, along with libraries such as NumPy, TensorFlow, or PyTorch
Target Audience
- Developers with an interest in AI and intelligent systems
- Data Scientists exploring reinforcement learning frameworks
- Machine Learning Engineers working with autonomous systems
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
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