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Duration 14 hours
Course Outline
Foundations of Reinforcement Learning
- An overview of reinforcement learning and its diverse applications
- Comparing supervised, unsupervised, and reinforcement learning paradigms
- Essential concepts: agents, environments, rewards, and policies
Markov Decision Processes (MDPs)
- Analyzing states, actions, rewards, and state transitions
- Exploring value functions and the Bellman Equation
- Applying dynamic programming to solve MDPs
Essential RL Algorithms
- Tabular approaches: Q-Learning and SARSA
- Policy-based strategies: The REINFORCE algorithm
- Actor-Critic frameworks and their real-world uses
Deep Reinforcement Learning
- Getting started with Deep Q-Networks (DQN)
- The role of experience replay and target networks
- Policy gradients and advanced deep RL techniques
RL Frameworks and Toolkits
- Introduction to OpenAI Gym and other RL environments
- Building RL models using PyTorch or TensorFlow
- Processes for training, testing, and benchmarking RL agents
Challenges in RL
- Balancing exploration and exploitation during training
- Addressing sparse rewards and credit assignment difficulties
- Managing scalability and computational constraints in RL
Practical Exercises
- Building Q-Learning and SARSA algorithms from the ground up
- Training a DQN-based agent to interact with simple games in OpenAI Gym
- Optimizing RL models for enhanced performance in custom environments
Conclusion and Future Directions
Requirements
- Solid grasp 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
