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ELEC-E8125 - Reinforcement learning D, Lecture, 13.9.2021-8.12.2021
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Course arrangements
Lecture 1: Overview
Lecture 2: Markov decision processes
Lecture 3: Reinforcement learning
Lecture 3 recording (Aalto login only)
Lecture 4: Function approximation
Lecture 4 recording (Aalto login only)
Lecture 5: Policy gradient
Lecture 5 recording (Aalto login only)
Lecture 6: Actor-critic methods
Lecture 6 recording (Aalto login only)
Lecture 7: Optimal control
Lecture 7 recording (Aalto login only)
Lecture 8: Model-based reinforcement learning
Lecture 8 recording (Aalto login only)
Guest lecture of Gökhan Alcan
Guest lecture of Gökhan Alcan recording (Aalto login only)
Guest Lecture on Entropy Regularization in Reinforcement Learning (Riad Akrour)
Guest Lecture on Entropy Regularization in Reinforcement Learning recording (Aalto login only)
Lecture 9: Partially observable Markov decision processes (POMDPs)
Lecture 9 recording (Aalto login only)
Lecture 10: Larger POMDPs
Lecture 10 recording (Aalto login only)
Sutton & Barto, Reinforcement Learning: An introduction, 2nd ed.
Extra slides: Planning in discrete space
Platt, Introduction to Linear Quadratic Regulation
Reinforcement learning course at UCL
Deep reinforcement learning course at UC Berkeley
LaValle, Planning Algorithms
Course arrangements
Lecture 1: Overview
Lecture 1 recording (requires Aalto account)
Lecture 2: Markov Decision Processes
Lecture 2 recording (Aalto login only)
Lecture 3: Reinforcement learning in discrete domains
Lecture 3 recording (Aalto login only)
Lecture 4: Function approximation
Lecture 5: Policy gradient
Lecture 5 recording (Aalto login only)
Lecture 6: Actor-critic methods
Lecture 6 recording (Aalto login only)
Lecture 7: Optimal control
Lecture 7 recording (Aalto login only)
Lecture 8: Model-based RL
Lecture 8 recording, only partial available (Aalto login only)
Guest lecture: Safety and optimal control (Gökhan Alcan)
Guest lecture recording (Aalto login only)
Guest lecture notes
Lecture 9: Partially Observable Markov Decision Processes
Lecture 9 recording (Aalto login only)
Lecture 10: Large POMDPs
Lecture 10 recording (Aalto login only)
Model-based reinforcement learning tutorial (ICML 2020)
Bradberry, "Introduction to Monte Carlo Tree Search"
Deep Reinforcement Learning: Pong from Pixels
AlphaGo: using machine learning to master the ancient game of Go
Andrew Ng, "Nuts and Bolts of Applying Deep Learning" (video)
Juliani, Simple Reinforcement Learning with Tensorflow
Guest lecture: Deep reinforcement learning in robotics
Exercise sessions on campus
Setting Things Up and Submission Instructions
Exercise intro slides
Exercise Report Latex Template
PyTorch intro
Exercise 1 - Reinforcement Learning introduction
Exercise Week 1 - Intro
Exercise 2 - Value iteration
Exercise 3 - Grid-based Q-Learning
Exercise 4 - Q-Learning with Function Approximation
Exercise 4 Intro
Mnih, Volodymyr, et al. "Playing Atari with Deep Reinforcement Learning". (2013)
Exercise 5 - Policy Gradient
Exercise 6 - Actor Critic
Quiz 1
Quiz 2
Quiz 3
Quiz 4
Quiz 5
Quiz 6
Quiz 7
RL Course Project
Code base for Part 1
Offline Dataset
Project submission
Project report
Alternative Course Project Proposal
Alternative Course Project report submission
Course arrangements ►
ELEC-E8125 - Reinforcement learning D, Lecture, 13.9.2021-8.12.2021
Sections
Reinforcement learning
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