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Course Outline
Fundamentals of Reinforcement Learning
- Overview of reinforcement learning concepts and their practical applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Essential concepts: agents, environments, rewards, and policy
Markov Decision Processes (MDPs)
- Exploring states, actions, rewards, and state transitions
- Value functions and the Bellman Equation
- Using dynamic programming to solve MDPs
Core RL Algorithms
- Tabular approaches: Q-Learning and SARSA
- Policy-based methods: The REINFORCE algorithm
- Actor-Critic frameworks and their usage
Deep Reinforcement Learning
- Getting started with Deep Q-Networks (DQN)
- Experience replay and target networks
- Policy gradients and advanced deep RL techniques
RL Frameworks and Tools
- 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
- Scalability and computational constraints in RL
Practical Activities
- Building Q-Learning and SARSA algorithms from the ground up
- Training a DQN-based agent to play a simple game within OpenAI Gym
- Optimising RL models for better performance in custom environments
Wrap-up and Future Directions
Requirements
- A solid command of machine learning principles and algorithms
- Proficiency in Python programming
- Working knowledge of neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- AI specialists
14 Hours
