What You’ll Learn: RL Fundamentals
You’ll go from understanding the RL framework (agents, environments, rewards) to implementing and training sophisticated RL algorithms.
Q-Learning & SARSA
Learn model-free value-based methods for discrete action spaces.
Policy Gradients
Implement policy-based methods for both discrete and continuous actions.
Deep RL (DQN, Actor-Critic)
Combine neural networks with RL algorithms for complex environments.
Agent Environments
Work with simulators like OpenAI Gym to train and test your agents.
Who Is This Course For?
Ideal for experienced ML engineers and researchers looking to specialize in decision-making AI and control systems.
- ML engineers wanting to add RL to their skillset
- Researchers interested in autonomous agents
- Developers working on robotics or game AI
Hands-On Projects
Grid World Q-Learning Agent
Train an agent to navigate a simple grid world using tabular Q-Learning.
Atari DQN Player
Implement and train a Deep Q-Network to play classic Atari games.
Capstone
Continuous Control Agent
Build an agent using Policy Gradients to control a simulated robotic arm.
4-Week RL Syllabus
~48 hours total • Lifetime LMS access • 1:1 mentor support
Week 1: Fundamentals & Q-Learning
- Introduction to RL concepts (agent, environment, reward)
- Markov Decision Processes (MDPs)
- Value functions and Bellman equations
- Tabular Q-Learning and SARSA algorithms
Week 2: Policy Gradients
- Policy-based vs. value-based methods
- REINFORCE algorithm
- Variance reduction techniques (baseline)
- Actor-Critic methods (introduction)
Week 3: Deep RL Basics
- Deep Q-Networks (DQN) and experience replay
- Target networks and Double DQN
- Deep Deterministic Policy Gradients (DDPG) basics
- Introduction to environment simulators (Gym)
Week 4: Advanced RL & Applications
- Proximal Policy Optimization (PPO) overview
- Exploration strategies
- Multi-agent RL concepts
- Capstone project: Advanced agent implementation
NSTC‑Accredited Certificate
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Frequently Asked Questions
Yes, a strong understanding of machine learning concepts (supervised/unsupervised learning), neural networks, and Python is essential. Familiarity with libraries like NumPy, Pandas, and ideally TensorFlow/PyTorch is required.
You will build several RL agents, including a Q-Learning agent for a grid world, a Deep Q-Network (DQN) for Atari games, and a Policy Gradient agent for continuous control tasks.
