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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)
The training level was high. The instructor was not afraid to use mathematical formalisms.
Mateusz Soczewka - Santander Bank Polska S.A.
Course - Fundamentals of Reinforcement Learning
Machine Translated
High level of theoretical and practical knowledge of the instructors. Communicative skills of the instructors. During the course, it was possible to ask questions and receive satisfactory answers.
Kamil Kurek - ING Bank Slaski S.A.; Kamil Kurek Programowanie
Course - Understanding Deep Neural Networks
Machine Translated
I liked the new insights in deep machine learning.
Josip Arneric
Course - Neural Network in R
The trainer explained difficult and advanced topics very clearly.
Leszek K
Course - Artificial Intelligence Overview
Machine Translated
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.
