Deep Reinforcement Learning
From foundational concepts to advanced algorithms, this Nanodegree equips you with the tools to build intelligent agents using Python, neural networks, and state-of-the-art RL frameworks across robotics, finance, and beyond.
What you'll learn
- Learn foundational and advanced deep reinforcement learning algorithms
- Build intelligent agents using Python and neural networks
- Apply RL frameworks to domains like robotics, finance, and games
- Complete hands-on projects such as navigation, continuous control, and multi‑agent tennis
Skills you'll gain
- Apply value-based reinforcement learning methods including Monte Carlo, SARSA, Q-Learning, and Expected SARSA
- Formulate tasks as Markov Decision Processes and reason about the exploration–exploitation dilemma
- Implement deep Q-networks with experience replay for complex environments
- Use policy-based and policy gradient methods, including REINFORCE and PPO
- Combine value-based and policy-based methods with actor–critic algorithms
- Train agents for continuous control tasks and multi-agent environments
- Understand and apply concepts like Markov games, multi-agent training, and AlphaZero
- Use PyTorch to build and train neural networks for deep RL applications
Prerequisites
- • Intermediate Python
- • Deep learning framework proficiency
- • Neural network basics
- • Object-oriented programming basics
- • Reinforcement learning fundamentals
- • Ability to communicate fluently and professionally in written and spoken English
Who this course is for
- → Learners with strong interest in deep reinforcement learning
- → Practitioners aiming to apply RL in robotics, finance, and other domains
- → Students comfortable learning in written and spoken English
Provider
Udacity
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