57 concepts · 8 modules
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Reinforcement Learning
Foundations through deep RL, policy gradients, model-based methods, RL for language models, and landmark applications.
Start Module 01Curriculum
A structured path through the course content.
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Module 01 8 concepts
Start here
Foundations
MDPs, reward signals, policies, and the RL framework.
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Module 02 7 concepts
Tabular Methods
Q-learning, SARSA, dynamic programming, and Monte Carlo methods.
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Module 03 7 concepts
Function Approximation & Deep RL
DQN, experience replay, and deep reinforcement learning.
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Module 04 8 concepts
Policy Gradient Methods
REINFORCE, PPO, A2C, and actor-critic methods.
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Module 05 6 concepts
Model-Based RL
World models, planning, and model-based approaches.
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Module 06 8 concepts
Advanced Methods
Hierarchical RL, multi-agent RL, and inverse RL.
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Module 07 7 concepts
RL for Language Models
RLHF, reward modeling, and RL in the LLM training pipeline.
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Module 08 6 concepts
Landmark Applications
AlphaGo, Atari, robotics, and milestone RL achievements.