Reinforcement Learning

The "Reinforcement Learning in Practice" training is an in-depth, hands-on workshop designed to equip participants with the knowledge and practical skills needed to effectively implement Reinforcement Learning (RL) techniques. Over three days, attendees will explore the fundamental principles of RL, understand key algorithms, and apply them to real-world problems. This workshop blends theoretical insights with coding exercises and case studies, ensuring participants gain conceptual clarity and practical experience building RL models.

Workshop Overview

Learning Outcomes

• Understand the fundamentals of Reinforcement Learning (RL)

• Explore RL key concepts: agents, environments, rewards, and policies.

• Implement basic RL algorithms like Q-Learning and SARSA.

• Apply policy-based and value-based methods for sequential decision-making.

• Balance exploration and exploitation trade-offs.

• Optimize RL models through hyperparameter tuning.

• Discuss real-world RL applications such as robotics, finance, ...

Detailed Course Schedule

• Day 1:

  • RL basics: agents, rewards, policies, environments.

  • Exploration vs. exploitation.

  • Q-Learning & Markov Decision Processes (MDPs).

• Day 2:

  • Value-based vs. policy-based methods.

  • SARSA, DQN, and Policy Gradient.

• Day 3:

  • RL in robotics, finance, and gaming.

  • Actor-critic, PPO, A3C methods.

  • Simulating RL environments.

• Utilize RL environments for experimentation.

• Exploring real-world applications in robotics, finance, gaming, and automation.

• Hands-on coding sessions using Python, OpenAI Gym, TensorFlow, or PyTorch.

• Simulating RL environments to develop problem-solving strategies.

• Learning industry best practices for deploying RL models in production.

What will it be about?