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Last updated:

September 26, 2023

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FREE

This course includes:

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Description

Deep Reinforcement Learning (Spring 2017, UC Berkeley). Instructors: Sergey Levine, John Schulman, and Chelsea Finn.

This course will assume some familiarity with reinforcement learning, numerical optimization and machine learning. The course covers topics: Supervised learning and decision making; Basic reinforcement learning: Q-learning and policy gradients; Advanced model learning and prediction; Advanced deep reinforcement learning: trust region policy gradients, actor-critic methods, exploration; Open problems and research talks.

Course Curriculum

  • Lecture 01 – Introduction Unlimited
  • Lecture 02 – Supervised Learning of Behaviors: Deep Learning, Dynamical Systems, and Behavior Cloning Unlimited
  • Lecture 03 – Optimal Control, Trajectory, Optimization, and Planning Unlimited
  • Lecture 04 – Learning Dynamical System Models from Data Unlimited
  • Lecture 05 – Learning Policies by Imitating Optimal Control Unlimited
  • Lecture 06 – Direct Collocation Methods for Trajectory Optimization and Policy Learning Unlimited
  • Lecture 07 – Markov Decision Processes and Solving Finite Problems Unlimited
  • Lecture 08 – Policy Gradient Methods Unlimited
  • Lecture 09 – Q-Function Learning Methods Unlimited
  • Lecture 10 – Advanced Q-Function Learning Methods Unlimited
  • Lecture 11 – Advanced Model Learning Unlimited
  • Lecture 12 – Advanced Topics in Imitation Learning and Safety Unlimited
  • Lecture 13 – Inverse Reinforcement Learning Unlimited
  • Lecture 14 – Advanced Policy Gradient Methods: Natural Gradient, TRPO, and More Unlimited
  • Lecture 15 – Variance Reduction for Policy Gradient Methods Unlimited
  • Lecture 16 – Policy Gradient Methods: Pathwise Derivative Methods and Wrap-Up Unlimited
  • Lecture 17 – The Exploration Problem Unlimited
  • Lecture 18 – Asynchronous and Parallel Algorithms Unlimited
  • Lecture 19 – Transfer in (Deep) Reinforcement Learning Unlimited
  • Lecture 20 – Neural Architecture Search with Reinforcement Learning Unlimited
  • Lecture 21 – Generalization and Safety in Reinforcement Learning and Control Unlimited
  • Lecture 22 – Deep Reinforcement Learning with Forward Prediction, Memory, and Hierarchy Unlimited
  • Lecture 23 – Towards a Unified View of Supervised Learning and Reinforcement Learning Unlimited
  • Lecture 24 – Adversarial Examples in Reinforcement Learning Unlimited
  • Lecture 25 – Review Unlimited

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