Introduction

Reinforcement Learning (RL) is at the forefront of AI-driven decision-making, enabling machines to learn optimal behaviors through trial and error. From robotics and self-driving cars to finance, healthcare, and gaming, RL is revolutionizing industries by solving complex, sequential decision problems.

This 5-day hands-on training provides participants with a deep dive into Markov Decision Processes (MDPs), policy optimization, deep reinforcement learning (DRL), and real-world applications using frameworks like OpenAI Gym, Stable-Baselines3, TensorFlow, and PyTorch.

Objectives

By the end of this course, participants will:

1. Understand core RL concepts, including MDPs, Q-learning, and policy gradients.
2. Implement value-based and policy-based RL algorithms.
3. Train Deep Q-Networks (DQNs) and actor-critic models.
4. Utilize OpenAI Gym and custom RL environments.
5. Apply RL to real-world problems in finance, robotics, and healthcare.
6. Optimize RL models using hyperparameter tuning and transfer learning.
7. Deploy RL agents in cloud environments and reinforcement learning-as-a-service.

Who Should Attend?

• Data Scientists & Machine Learning Engineers
• AI Researchers & Deep Learning Practitioners
• Software Developers & Game AI Engineers
• Robotics, Finance, and Healthcare AI Specialists
• Anyone looking to apply RL to real-world challenges

Course Outline (5 Days)

Day 1: Fundamentals of Reinforcement Learning

Morning Session

• Introduction to RL

  • Key concepts: MDPs, reward functions, policy, value functions
  • Difference between supervised learning, unsupervised learning, and RL
  • Hands-on: Building an RL agent for a simple game environment

• Markov Decision Processes (MDPs) & Dynamic Programming

  • Understanding states, actions, rewards, and policies
  • Bellman equations and dynamic programming methods
  • Hands-on: Solving a grid-world environment with dynamic programming

Afternoon Session

• Model-Free RL: Monte Carlo & Temporal Difference Learning

  • First-visit and every-visit Monte Carlo methods
  • TD(0), TD(λ), and n-step learning
  • Hands-on: Training an RL agent using Monte Carlo methods

• Hands-on Exercise

  • Implementing temporal difference learning in OpenAI Gym

Day 2: Q-Learning & Deep Q-Networks (DQN)

Morning Session

• Q-Learning and SARSA

  • Off-policy vs. on-policy learning
  • ε-Greedy exploration vs. softmax policy
  • Hands-on: Implementing Q-learning from scratch in Python

• Deep Q-Networks (DQN)

  • Combining deep learning and reinforcement learning
  • Experience replay & target networks
  • Hands-on: Building a DQN agent to play Atari games

Afternoon Session

• Advanced DQN Techniques

  • Double DQN, Dueling DQN, and Prioritized Experience Replay
  • Stability and convergence challenges
  • Hands-on: Improving DQN with Double Q-learning

• Hands-on Exercise

  • Training a DQN agent for stock market trading simulations

Day 3: Policy Gradient Methods & Actor-Critic Models

Morning Session

• Policy-Based Methods

  • REINFORCE algorithm & stochastic policies
  • Pros and cons of value-based vs. policy-based methods
  • Hands-on: Implementing REINFORCE for robotic control tasks

• Actor-Critic Algorithms

  • Advantage Actor-Critic (A2C) & Asynchronous Advantage Actor-Critic (A3C)
  • Parallel training and sample efficiency
  • Hands-on: Building an A2C agent for continuous action spaces

Afternoon Session

• Proximal Policy Optimization (PPO) & Trust Region Policy Optimization (TRPO)

  • Improving stability with PPO and TRPO
  • Hyperparameter tuning for policy optimization
  • Hands-on: Training a PPO agent for real-time decision-making tasks

• Hands-on Exercise

  • Building an RL agent for autonomous driving simulations

Day 4: Advanced RL Techniques & Real-World Applications

Morning Session

• Hierarchical Reinforcement Learning (HRL)

  • Sub-goals and options framework
  • HRL for complex decision-making tasks
  • Hands-on: Applying HRL to multi-stage robotic tasks

• Multi-Agent Reinforcement Learning (MARL)

  • Cooperative and competitive RL
  • Training multiple RL agents in the same environment
  • Hands-on: Developing a multi-agent RL system for game AI

Afternoon Session

• RL for Robotics & Industrial Automation

  • Sim2Real transfer and domain adaptation
  • Using Mujoco and PyBullet for robotics RL
  • Hands-on: Training a robotic arm for object manipulation

• Hands-on Exercise

  • Applying RL for energy-efficient smart grid optimization

Day 5: RL Deployment, Cloud Services & Capstone Project

Morning Session

• Deploying RL Models in Production

  • RL model inference and serving strategies
  • Using RL-as-a-Service on AWS, Azure, and Google Cloud
  • Hands-on: Deploying an RL model using TensorFlow Serving

• Transfer Learning & Meta-Reinforcement Learning

  • Few-shot learning for RL agents
  • Hands-on: Applying transfer learning to fine-tune RL models

Afternoon Session

• Capstone Project & Final Presentations

  • Choose from:
    1. Developing an RL-based chatbot for customer interactions
    2. Optimizing a logistics network with reinforcement learning
    3. Creating an RL-powered recommendation system
  • Participants present their projects & receive expert feedback

• Certification & Networking Session

Post-Course Benefits

• Hands-on experience with real-world RL applications
• Mastery of DQNs, policy gradients, and multi-agent RL
• Expertise in deploying RL models on cloud platforms
• Portfolio-ready projects for AI careers
• Exclusive access to RL resources, datasets, and GitHub repositories.