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Course Outline
Foundations of Reinforcement Learning
- Broad overview of RL and its industry applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Core concepts: agent, environment, rewards, and policy
Markov Decision Processes (MDPs)
- Exploring states, actions, rewards, and state transitions
- Value functions and the application of the Bellman Equation
- Solving MDPs using dynamic programming techniques
Essential RL Algorithms
- Tabular approaches: Q-Learning and SARSA
- Policy-based techniques: the REINFORCE algorithm
- Actor-Critic frameworks and their practical uses
Deep Reinforcement Learning
- Overview of Deep Q-Networks (DQN)
- Techniques for experience replay and target networks
- Policy gradients and advanced deep RL methodologies
RL Ecosystem: Frameworks and Tools
- Getting started with OpenAI Gym and other RL environments
- Developing RL models using PyTorch or TensorFlow
- Processes for training, testing, and benchmarking RL agents
Navigating RL Complexities
- Strategies for balancing exploration and exploitation
- Managing sparse rewards and credit assignment issues
- Addressing scalability and computational constraints
Practical Implementation
- Building Q-Learning and SARSA algorithms from the ground up
- Training a DQN-based agent to play games in OpenAI Gym
- Optimizing RL models for better performance in custom settings
Recap and Future Directions
Requirements
- Solid command of machine learning principles and algorithms
- Advanced proficiency in Python programming
- Working knowledge of neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- AI specialists
14 Hours
