What if machines could learn not just from data, but from experience? Deep Reinforcement Learning (DRL) is the bridge between deep learning and dynamic, interactive environments. It’s the technology behind AlphaGo, autonomous robotics, recommendation engines, and AI agents that learn to play complex games—or even run simulations in finance, logistics, and supply chains.

This course provides a deep yet approachable dive into DRL. You’ll start with the foundational ideas of agents, environments, rewards, and policies before exploring Q-learning, policy gradients, and actor-critic methods. From there, you’ll build agents in simulated environments like OpenAI Gym, train them using deep neural networks, and optimize their behavior through feedback loops.

Emphasis is placed on exploration-exploitation tradeoffs, reward engineering, stability in training, and real-world transferability. You’ll train agents to play games, control robots, and make financial decisions—all while understanding the mathematics and architectures that make it possible.

By the end, you’ll be ready to build adaptive systems that learn on their own, paving the way for research, innovation, or applied solutions in edge computing, robotics, game development, and more.

Delivery
Courses are delivered 100% online. Learn on your schedule — videos, case studies, and templates are available instantly upon enrollment. All content is optimized for mobile and desktop.

Refunds
Request a full refund within 30 days if you’re not confident in applying DRL concepts to real or simulated environments.

Language
English

Curriculum 
Module 1: Foundations of Reinforcement Learning – Markov decision processes, rewards, exploration.
Module 2: Deep Q-Learning – Q-tables, DQNs, target networks, replay buffers.
Module 3: Policy-Based & Actor-Critic Methods – REINFORCE, A3C, PPO, stability tricks.
Module 4: Environments & Applications – OpenAI Gym, multi-agent settings, robotics, finance.
Capstone Simulation: Build and train a DRL agent that learns to optimize a task (e.g., game strategy, inventory control, or robotic motion) with measurable performance improvements.