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Duration 14 hours
Course Outline
Introduction to Reinforcement Learning
- Overview of reinforcement learning and its real-world applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Core concepts: agents, environments, rewards, and policies
Markov Decision Processes (MDPs)
- Exploring states, actions, rewards, and state transitions
- Value functions and the Bellman Equation
- Applying dynamic programming to solve MDPs
Core RL Algorithms
- Tabular approaches: Q-Learning and SARSA
- Policy-based strategies: The REINFORCE algorithm
- Actor-Critic frameworks and their practical use cases
Deep Reinforcement Learning
- Overview of Deep Q-Networks (DQN)
- Techniques for experience replay and target networks
- Policy gradients and advanced deep RL methodologies
RL Frameworks and Tools
- Getting started with OpenAI Gym and other RL environments
- Developing RL models using PyTorch or TensorFlow
- Procedures for training, testing, and benchmarking RL agents
Challenges in RL
- Strategies for balancing exploration and exploitation during training
- Managing sparse rewards and credit assignment issues
- Overcoming scalability and computational constraints in RL
Practical Activities
- Building Q-Learning and SARSA algorithms from scratch
- Training a DQN-based agent to perform tasks in OpenAI Gym
- Optimizing RL models for better performance in custom environments
Conclusion and Future Directions
Requirements
- A robust understanding of machine learning principles and algorithms
- Proficiency in Python programming
- Experience with neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- AI specialists
