Reinforcement Learning
MDPs and value functions, dynamic programming, model-free methods, and policy gradients.
0 / 17 solved
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MDPs & Value Functions
States, actions, rewards, the Bellman equations, and value vs Q functions.
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Dynamic Programming
Value iteration and policy iteration.
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Model-Free Methods
Q-learning, SARSA, temporal-difference learning, exploration vs exploitation.
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Policy Gradient Methods
REINFORCE, actor-critic, advantage estimation and GAE, the PPO idea.
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