Intelligent Agent Development with Deep RL Course

Name: Intelligent Agent Development with Deep Reinforcement Learning and OpenAI Gym
Availability: InStock

Inspired by: Inspired by Praveen Palanisamy's Hands-On Intelligent Agents with OpenAI Gym

Intelligent Agent Development with Deep RL Course

At a glance

Duration: 5 days
Format: Classroom
Cities: Rome, Madrid, Sharm El-Sheikh, Montreux, Cairo, Amsterdam and more
Next session: 12 – 16 October 2026, Rome
Average fee: 5,800 €

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Intelligent Agent Development with Deep Reinforcement Learning and OpenAI Gym

Course Overview

The course is an immersive, hands-on training designed for professionals who wish to build AI systems using OpenAI Gym and deep reinforcement learning techniques. Based on the comprehensive book Hands-On Intelligent Agents with OpenAI Gym, this course offers a step-by-step practical journey through developing intelligent agents that solve real-world tasks such as game playing, robotics simulation, and autonomous driving. Key topics include Q-learning, Deep Q-Learning, experience replay, actor-critic methods, and environment customisation. Covering essential platforms like PyTorch, TensorBoard, CARLA, Roboschool, Gym-Retro, and MuJoCo, participants will acquire the skills to implement agents for both discrete and continuous action spaces.

Target Audience

AI/ML Engineers and Developers
Robotics Engineers
Data Scientists interested in RL
Software Engineers exploring AI agents
Game Developers

Targeted Organizational Departments

AI Research and Development Units
Robotics and Automation Teams
Innovation Labs
Software Engineering Departments
Simulation and Gaming Divisions

Targeted Industries

Automotive (autonomous vehicles)
Robotics and Industrial Automation
Gaming and Simulation
Aerospace and Defence
Healthcare Tech (for training intelligent diagnostics agents)

Course Offerings

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

Set up and use OpenAI Gym and custom environments
Apply Q-learning and Deep Q-learning using PyTorch
Train agents using experience replay and epsilon-greedy policies
Customise gym environments, including CARLA and MuJoCo
Visualise training progress with TensorBoard
Understand and apply policy gradients, actor-critic, PPO, and Rainbow RL
Build and test agents on Atari games and Gym-Retro environments
Monitor and optimise performance with reward shaping and preprocessing techniques

Training Methodology

This course employs an applied, project-based methodology combining theoretical foundations with real-world practice. Learners will engage in interactive tutorials, group-based agent-building exercises, live demonstrations, and guided reinforcement learning projects. Emphasis is placed on practical implementation using PyTorch, JSON config files, CUDA acceleration, and OpenAI Gym. Case studies on Mountain Car, Cart Pole, Atari games, and CARLA simulations will illustrate key learning principles. Feedback sessions, breakout discussions, and reflective reviews ensure retention and hands-on mastery.

Course Toolbox

OpenAI Gym Environments Library
PyTorch Deep Learning Framework
Conda and CUDA Setup Guides
TensorBoard for monitoring
JSON templates for hyperparameters
Atari and Gym-Retro emulators
CARLA autonomous driving simulator
Sample agent architectures (DQN, PPO, DDPG, Rainbow)
Pre-built notebooks and implementation guides

Course Agenda

Day 1: Foundations of Intelligent Agents & Reinforcement Learning

Topic 1: Introduction to Intelligent Agents and Learning Environments
Topic 2: Exploring the Capabilities and Interface of OpenAI Gym
Topic 3: Categories of Gym Tasks: From Classic Control to Robotics
Topic 4: Setting Up Your Python, Conda, CUDA, and PyTorch Environments
Topic 5: Deep Dive into Reinforcement Learning and MDPs
Topic 6: Understanding the Policy, Value Functions, and Exploration Strategies
Reflection & Review: Fundamentals of AI Agents and Environment Interaction

Day 2: Hands-On with Q-Learning and Deep Q-Learning

Topic 1: Solving the Mountain Car Problem Using Q-Learning
Topic 2: Implementing Q-learning with NumPy and Hyperparameter Tuning
Topic 3: Transition to Deep Q-Learning using PyTorch
Topic 4: Applying Experience Replay and Epsilon-Greedy Policies
Topic 5: Stabilizing Learning with Target Networks
Topic 6: Visualizing Agent Performance Using TensorBoard
Reflection & Review: Comparing Traditional and Deep Q-Learning Approaches

Day 3: Custom Environments and Real-World Applications

Topic 1: Creating Custom Gym Environments with Templates and Registration
Topic 2: Building the CARLA Driving Simulator as a Gym-Compatible Environment
Topic 3: Implementing Reset, Step Functions, and Sensor Integration
Topic 4: Managing Discrete vs Continuous Action Spaces in CARLA
Topic 5: Real-Time Testing and Visualization of Simulation-Based Environments
Topic 6: Techniques for Accessing and Using Camera/Sensor Data
Reflection & Review: Environment Design for Reinforcement Learning Agents

Day 4: Advanced Agents with Actor-Critic Algorithms

Topic 1: Fundamentals of Policy Gradients and Actor-Critic Architectures
Topic 2: Implementing n-Step Advantage Actor-Critic Algorithms
Topic 3: Designing Actor and Critic Networks for Autonomous Agents
Topic 4: Logging, Monitoring, and Saving Model Progress
Topic 5: Training Actor-Critic Agents in the CARLA Simulator
Topic 6: Exploring Synchronous vs Asynchronous Implementations
Reflection & Review: From Theory to Practice in Actor-Critic Training

Day 5: The Learning Landscape – PPO, DDPG, Rainbow & Beyond

Topic 1: Proximal Policy Optimization (PPO) – Concepts and Use Cases
Topic 2: Deep Deterministic Policy Gradient (DDPG) and Continuous Control
Topic 3: The Rainbow Algorithm: Integrating Value-Based Enhancements
Topic 4: Implementing Prioritized Replay, Dueling Nets, and Distributional RL
Topic 5: Roboschool, Gym-Retro, DeepMind Lab, and StarCraft II Environments
Topic 6: Comparative Insights Across Algorithms and Environment Suites
Reflection & Review: Capstone Discussion on Agent Development and Deployment

FAQ

What specific qualifications or prerequisites are needed for participants before enrolling in the course?

A working knowledge of Python and basic understanding of machine learning principles is recommended. Familiarity with NumPy and neural networks will help but is not mandatory.

How long is each day's session, and is there a total number of hours required for the entire course?

Each day's session is generally structured to last around 4-5 hours, with breaks and interactive activities included. The total course duration spans five days, approximately 20-25 hours of instruction.

Why does Deep Q-Learning use a target network and experience replay?

Target networks stabilise learning by keeping a fixed Q-target during updates. Experience replay improves sample efficiency and breaks temporal correlations in the training data, which helps avoid divergence in Q-learning.

How This Course is Different from Other Intelligent Agent Development Courses

Unlike generic AI courses, this program is uniquely grounded in the proven methodologies and real-world examples from the Hands-On Intelligent Agents with OpenAI Gym book. It emphasises practical, code-level implementations of OpenAI Gym tutorial-based environments like Mountain Car and Cart Pole, uses PyTorch RL agent implementation techniques, and incorporates TensorBoard for reinforcement learning progress visualisation. By covering a diverse algorithm landscape, including Rainbow RL, PPO, and DDPG, it ensures a holistic skill set.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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