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.
Duration 14 hours
Course Outline
Introduction to Reinforcement Learning
- Overview of reinforcement learning and its key applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Essential concepts: agent, environment, rewards, and policy
Markov Decision Processes (MDPs)
- Comprehending 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
- Introduction to Deep Q-Networks (DQN)
- Experience replay and target networks
- Policy gradients and advanced deep RL methodologies
RL Frameworks and Tools
- Overview of OpenAI Gym and other RL environments
- Utilizing PyTorch or TensorFlow for developing RL models
- Training, testing, and benchmarking RL agents
Challenges in RL
- Balancing exploration and exploitation during training
- Managing sparse rewards and credit assignment problems
- Scalability and computational hurdles in RL
Practical Exercises
- Building Q-Learning and SARSA algorithms from the ground up
- Training a DQN-based agent to play a simple game within 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
- Proficiency in Python programming
- Knowledge of neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- AI specialists
