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Course Outline
Introduction to Reinforcement Learning
- Overview of RL and its real-world applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Core concepts: agent, environment, rewards, and policy
Markov Decision Processes (MDPs)
- Analyzing states, actions, rewards, and state transitions
- Value functions and the Bellman Equation
- Solving MDPs using dynamic programming
Essential RL Algorithms
- Tabular approaches: Q-Learning and SARSA
- Policy-based strategies: The REINFORCE algorithm
- Actor-Critic frameworks and their use cases
Deep Reinforcement Learning
- Overview of Deep Q-Networks (DQN)
- Experience replay and target networks
- Policy gradients and advanced deep RL methodologies
RL Frameworks and Tooling
- Introduction to OpenAI Gym and other RL environments
- Developing RL models using PyTorch or TensorFlow
- Training, testing, and benchmarking RL agents
Challenges in RL
- Striking a balance between exploration and exploitation during training
- Handling sparse rewards and credit assignment issues
- Addressing scalability and computational demands in RL
Practical Exercises
- Building Q-Learning and SARSA algorithms from the ground up
- Training a DQN-based agent to play a simple game in OpenAI Gym
- Optimizing RL models for enhanced performance in custom environments
Conclusion and Future Directions
Requirements
- A robust command of machine learning principles and algorithms
- Proficiency in Python programming
- Knowledge of neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- AI specialists
14 Hours
