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 key applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Core concepts: agent, environment, rewards, and policy
Markov Decision Processes (MDPs)
- Analyzing 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 methods: The REINFORCE algorithm
- Actor-Critic frameworks and their practical uses
Deep Reinforcement Learning
- Overview of 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
- Handling sparse rewards and credit assignment issues
- Scalability and computational hurdles 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 enhanced performance in custom environments
Summary and Next Steps
Requirements
- A solid grasp of machine learning principles and algorithms
- Advanced proficiency in Python programming
- Knowledge of neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- AI specialists
14 Hours
