Reinforcement Learning Workshop at Rachis Systems

Overview

We are offering a 3-day internal workshop focused on Reinforcement Learning (RL) at Rachis Company. Led by our internal AI team, this workshop will introduce RL concepts and teach practical RL implementation.

            Workshop Details
            Duration: 3 Days ( 4 hours/day)
Dates: June 1st, 2nd, and 3rd, 2025
Time: 2:00 PM - 6:00 PM
Audience: Students / Developers with experience in basic Machine Learning and Neural Network fundamentals
Facilitators: Internal AI team
Tools: Python, Google Colab
Cost: 200,000 Syrian Pounds

          

Objectives

Build foundational understanding of Reinforcement Learning and its key components
Demonstrate RL applications in real-world environments
Apply RL techniques to a selected problem
Understand the core principles and terminology of reinforcement learning
Implement and experiment with fundamental RL algorithms
Frame business and real-world problems as RL tasks
Apply RL methods to practical environments
Employee-driven ideation on future use cases
Strengthened culture of technical innovation at Rachis

Location

Agenda

1Day 1: Introduction to Reinforcement Learning

What is Reinforcement Learning?
Why is RL needed? Difference between RL and ML
Key concepts: agent, environment, reward, state, action
Markov Decision Process (MDP)
Q-Learning Implementation on a basic Environment

2Day 2: Practical Deep RL Lab – DQN Implementation

Introduction to Deep Reinforcement Learning (Deep RL)
Overview of Deep Q-Networks (DQN): architecture and intuition
Hands-on Lab: Guided step-by-step DQN implementation (block by block)
- Break down core DQN blocks: replay buffer, Q-network, target network updates
- Participants write and assemble code for each component
Select a simple OpenAI Gym environment (e.g., CartPole) to test your DQN implementation
Experiment with training the agent and observe its learning process

3Day 3: Practical Lab – RL in V2X Communications with DDPG

Introduction to Rachis's V2X communication project and environment
Implementation Exercise: Build stub agents for the project (random, round robin, periodic)
Introduction to DDPG (Deep Deterministic Policy Gradient): concepts and applications in continuous action domains
Hands-on Lab: Guided step-by-step DDPG implementation (block by block)
- Construct essential DDPG modules (actor/critic networks, replay buffer, target networks)
- Write and assemble code for each component
- Integrate with the provided V2X environment
Train your own agent on the company's V2X scenario and compare with stub agents

Why Attend This Workshop?

🔍

Explore Reinforcement Learning

Learn core concepts and real-world applications of Reinforcement Learning

💡

Develop Practical Skills

Apply theoretical knowledge to real programming challenges

🚀

Enhance Company Projects

Contribute to developing innovative solutions to improve Rachis projects

🤝

Collaborate with Experts

Opportunity to interact and learn from the internal AI team

Technologies Used

Python

Google Colab

OpenAI Gym

TensorFlow/PyTorch

Outstanding Participant Award

🏆

Excellence Reward

At the end of the workshop, the most outstanding participant will receive:

💰 Full Refund of the workshop fee
📝 Training Contract opportunity at Rachis Systems

Selection will be based on participation, project implementation, and innovative thinking throughout the workshop.

Reinforcement Learning Workshop