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Duration 14 hours
Course Outline
Fundamentals of Reinforcement Learning
- A broad overview of reinforcement learning and its diverse applications
- Distinguishing between supervised, unsupervised, and reinforcement learning approaches
- Core concepts: agent, environment, reward signals, and policy
Markov Decision Processes (MDPs)
- Analyzing states, actions, rewards, and state transitions
- Exploring value functions and the Bellman Equation
- Applying dynamic programming techniques to solve MDPs
Essential RL Algorithms
- Tabular methods: Implementing Q-Learning and SARSA
- Policy-based approaches: The REINFORCE algorithm
- Actor-Critic architectures and their practical uses
Deep Reinforcement Learning
- Overview of Deep Q-Networks (DQN)
- Mechanisms of experience replay and target networks
- Policy gradients and sophisticated deep RL methodologies
RL Frameworks and Toolsets
- Getting started with OpenAI Gym and other RL environments
- Building RL models using PyTorch or TensorFlow
- Procedures for training, testing, and benchmarking RL agents
Navigating RL Challenges
- Striking the balance between exploration and exploitation during training
- Handling sparse rewards and credit assignment dilemmas
- Addressing scalability and computational constraints in RL
Practical Workshops
- Coding Q-Learning and SARSA algorithms from the ground up
- Training a DQN-based agent to play simple games within OpenAI Gym
- Optimizing RL models for enhanced performance in customized environments
Wrap-up and Future Directions
Requirements
- A solid command of core machine learning principles and algorithms
- Advanced proficiency in Python programming
- Working knowledge of neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- Specialists in artificial intelligence
