Reinforcement Learning for AI Agents

• Strong understanding of machine learning principles and algorithms
• Proficiency in Python programming
• Familiarity with neural networks and deep learning frameworks

Audience

• Machine learning engineers
• AI specialists

Reinforcement Learning (RL) is a cornerstone of modern AI research and applications. It focuses on training agents to make optimal decisions in dynamic, multi-step environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI professionals who wish to master reinforcement learning techniques and implement them for training AI agents in solving complex problems.

By the end of this training, participants will be able to:

• Understand the core principles of reinforcement learning and Markov Decision Processes (MDPs).
• Design and implement RL algorithms such as Q-Learning, SARSA, and Deep Q-Networks (DQN).
• Utilize frameworks like OpenAI Gym and RL libraries for practical applications.
• Train AI agents to solve real-world, multi-step decision-making problems.
• Address challenges such as exploration-exploitation trade-offs and convergence in RL training.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Reinforcement Learning

• Overview of reinforcement learning and its applications
• Differences 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
• Dynamic programming for solving MDPs

Core RL Algorithms

• Tabular methods: Q-Learning and SARSA
• Policy-based methods: REINFORCE algorithm
• Actor-Critic frameworks and their applications

Deep Reinforcement Learning

• Introduction to Deep Q-Networks (DQN)
• Experience replay and target networks
• Policy gradients and advanced deep RL methods

RL Frameworks and Tools

• Introduction to OpenAI Gym and other RL environments
• Using PyTorch or TensorFlow for RL model development
• Training, testing, and benchmarking RL agents

Challenges in RL

• Balancing exploration and exploitation in training
• Dealing with sparse rewards and credit assignment problems
• Scalability and computational challenges 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 for improved performance in custom environments

Summary and Next Steps