Course Outline
1. Overview of Deep Reinforcement Learning
- Defining Reinforcement Learning
- Distinctions between Supervised, Unsupervised, and Reinforcement Learning
- Emerging DRL applications in 2025 (spanning robotics, healthcare, finance, and logistics)
- Navigating the agent-environment interaction cycle
2. Core Concepts in Reinforcement Learning
- Markov Decision Processes (MDP)
- Key components: State, Action, Reward, Policy, and Value functions
- Balancing Exploration and Exploitation
- Monte Carlo techniques and Temporal-Difference (TD) learning
3. Implementing Fundamental RL Algorithms
- Tabular approaches: Dynamic Programming, Policy Evaluation, and Iteration
- Q-Learning and SARSA implementations
- Epsilon-greedy exploration techniques and decay strategies
- Creating RL environments with OpenAI Gymnasium
4. Advancing to Deep Reinforcement Learning
- Limitations inherent in tabular methods
- Utilizing neural networks for function approximation
- Deep Q-Network (DQN) structure and operational workflow
- Experience replay mechanisms and target networks
5. Sophisticated DRL Algorithms
- Advanced variants: Double DQN, Dueling DQN, and Prioritized Experience Replay
- Policy Gradient approaches, including the REINFORCE algorithm
- Actor-Critic architectures (A2C, A3C)
- Proximal Policy Optimization (PPO)
- Soft Actor-Critic (SAC)
6. Managing Continuous Action Spaces
- Specific challenges in continuous control tasks
- Applying DDPG (Deep Deterministic Policy Gradient)
- Twin Delayed DDPG (TD3)
7. Essential Tools and Frameworks
- Leveraging Stable-Baselines3 and Ray RLlib
- Tracking and monitoring via TensorBoard
- Hyperparameter tuning techniques for DRL models
8. Reward Design and Environment Creation
- Reward shaping and penalty calibration
- Developing custom environments within Gymnasium
9. Handling Partially Observable Environments and Generalization
- Addressing incomplete state information (POMDPs)
- Memory-based strategies using LSTMs and RNNs
- Enhancing agent robustness and generalization capabilities
10. Game Theory and Multi-Agent Reinforcement Learning
- Introduction to multi-agent setups
11. Case Studies and Industry Applications
12. Troubleshooting and Performance Optimization
13. Conclusion and Future Directions
Requirements
- Strong proficiency in Python programming
- Solid understanding of Calculus and Linear Algebra
- Foundational knowledge of Probability and Statistics
- Experience in developing machine learning models using Python along with NumPy or TensorFlow/PyTorch
Target Audience
- Developers eager to explore AI and intelligent systems
- Data Scientists investigating reinforcement learning frameworks
- Machine Learning Engineers focused on autonomous systems
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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