Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Reinforcement Learning
- Overview of reinforcement learning and its practical applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Key concepts: agent, environment, rewards, and policy
Markov Decision Processes (MDPs)
- Understanding states, actions, rewards, and state transitions
- Value functions and the Bellman Equation
- Using dynamic programming to solve MDPs
Core RL Algorithms
- Tabular methods: Q-Learning and SARSA
- Policy-based methods: The REINFORCE algorithm
- Actor-Critic frameworks and their use cases
Deep Reinforcement Learning
- Introduction to Deep Q-Networks (DQN)
- Experience replay and target networks
- Policy gradients and advanced deep RL techniques
RL Frameworks and Tools
- Introduction to OpenAI Gym and other RL environments
- Developing RL models using PyTorch or TensorFlow
- Training, testing, and benchmarking RL agents
Challenges in RL
- Balancing exploration and exploitation during training
- Addressing sparse rewards and credit assignment problems
- Scalability and computational hurdles in RL
Hands-On Activities
- Implementing Q-Learning and SARSA algorithms from scratch
- Training a DQN-based agent to play a simple game in OpenAI Gym
- Fine-tuning RL models to enhance performance in custom environments
Summary and Next Steps
Requirements
- A solid command 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
14 Hours
