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Duration 14 hours
Course Outline
Introduction to Reinforcement Learning
- An overview of reinforcement learning and its various applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Essential 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 methods: Q-Learning and SARSA
- Policy-based approaches: the REINFORCE algorithm
- Actor-Critic frameworks and their use cases
Deep Reinforcement Learning
- An introduction to Deep Q-Networks (DQN)
- 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
- Training, testing, and benchmarking RL agents
Challenges in RL
- Striking the right balance between exploration and exploitation during training
- Managing sparse rewards and credit assignment issues
- Addressing scalability and computational demands in RL
Hands-On Activities
- Building Q-Learning and SARSA algorithms from the ground up
- Training a DQN-based agent to play a simple game in OpenAI Gym
- Optimizing RL models for better performance in custom environments
Summary and Next Steps
Requirements
- A solid grasp of machine learning principles and algorithms
- Proficiency in Python programming
- Working knowledge of neural networks and deep learning frameworks
Audience
- Machine learning engineers
- AI specialists
