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DeepMind RL Course — Reinforcement Learning Study Pack
Use this as a lecture map before watching, a review guide between sessions, or a timestamped index when you need to revisit one concept. The DeepMind RL course is taught by David Silver.
Interactive DeepMind RL study pack
The DeepMind RL course covers Markov Decision Processes, dynamic programming, model-free prediction and control, TD learning, and policy gradient methods.
Dynamic programming introduces value iteration and policy iteration for environments with known transition dynamics.
Monte Carlo methods estimate value functions from experience, without requiring a model of the environment.
Temporal Difference learning combines bootstrapping with sampling to update value estimates after each step.
On-policy control with SARSA and off-policy control with Q-learning represent two fundamental tradeoffs in RL.
Policy gradient methods directly optimize the policy parameters using gradient ascent on expected return.
What this lesson teaches
The DeepMind Reinforcement Learning course by David Silver provides a rigorous introduction to RL theory and algorithms. The course covers the full spectrum from classical dynamic programming (when the environment model is known) through Monte Carlo and TD methods (model-free), and culminates in policy gradient algorithms. Each approach is motivated by a different information structure about the environment.
Key concepts and takeaways
- An MDP is defined by states, actions, transition probabilities, rewards, and a discount factor; the agent's goal is to find a policy maximizing expected cumulative reward.
- Value iteration applies the Bellman optimality equation iteratively to compute the optimal value function and extract the optimal policy.
- Monte Carlo estimation averages returns from complete episodes, requiring no model and working for any start state.
- TD learning combines Monte Carlo's sampling with bootstrapping, updating estimates based on other estimates rather than only on actual returns.
- Q-learning is off-policy — it learns the optimal value function while following a behavior policy that may explore more.
- Policy gradient methods express the policy as a parameterized function and update parameters by following gradients of expected return.
Review checklist
Implement value iteration for a known MDP (e.g., Gridworld) and verify the resulting value function and policy.
Implement TD(0) and compare its convergence speed to Monte Carlo on a simple episodic task.
Implement Q-learning with epsilon-greedy exploration and observe how the learned Q-values converge to the optimal values.
Implement REINFORCE for a simple task and analyze how variance in return estimates affects learning stability.
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