Deep Reinforcement Learning Prof. Kuan-Ting Lai 2020/3/5 Course - PowerPoint PPT Presentation

Jul 09, 2023 •105 likes •210 views

Course Requirements of Deep Reinforcement Learning Prof. Kuan-Ting Lai 2020/3/5 Course Requirements Kaggle-style homework (60%) TBD VizDoom Microsoft AirSim Final Project (40%) Team members (1 ~ 4) Final report + Demo

Course Requirements of Deep Reinforcement Learning Prof. Kuan-Ting Lai 2020/3/5
Course Requirements • Kaggle-style homework (60%) − TBD − VizDoom − Microsoft AirSim • Final Project (40%) − Team members (1 ~ 4) − Final report + Demo + Source code • Attendance (5%) − Roll call − Answering questions 2
Textbooks & References • Maxim Lapan , “Deep Reinforcement Learning Hands - on,” Packt, 2018 • Richard S. Sutton and Andrew G. Barto, “Reinforcement Learning, An Introduction, 2 nd Edition” The MIT Press, 2018 • Latest publications on Nature, CVPR, NIPS, ICML, AAAI, ICLR https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On 3
Schedule Date Syllabus 3/6 Introduction to Deep Reinforcement Learning (Sutton (2018), Chapter 1, 2) 3/13 Finite Markov Decision Processes and Dynamic Programming (Sutton (2018), Chapter 3, 4) HW1 TBD 3/20 PyTorch & OpenAI Gym (Lapan (2018), Chapter 2, 3) 3/27 Dynamic Programming & Monte Carlo Methods (Sutton (2018), Chapter 4, 5) 4/3 Temporal-Difference Learning (SARSA, Q-learning) (Sutton (2018), Chapter 6) HW2 TBD 4/10 Deep Q-Networks (Lapan (2018), Chapter 6, 7) 4/17 Policy Gradients (Lapan (2018), Chapter 9) 4/24 Actor-Critic Method (Lapan (2018), Chapter 10) HW3 Stocks Trading using RL 4
Schedule (cont.) Date 4/28 No Midterm, No Class 5/1 Final Project Proposal Due 5/8 A3C and A2C (Lapan (2018), Chapter 11 and OpenAI paper) 5/15 Continuous Action Space (Lapan (2018), Chapter 14) 5/22 Trust Regions – TRPO, PPO, and ACKTR (Lapan (2018), Chapter 15) HW4 Playing a Shooting Game (VizDoom) (Due 12/15) 5/29 Black-Box Optimization in RL ((Lapan (2018), Chapter 16) 6/5 Beyond Model-free (Lapan (2018), Chapter 17) 6/12 AlphaGo Zero (Lapan (2018), Chapter 18) 6/19 Final Project Demo 1 (20 mins, talk + demo, in English) 6/26 Final Project Demo 2 (20 mins, talk + demo, in English) 5
Grading Policy of Homework Kaggle Ranking Grade Description Grade Top 5% Excellent A+ 5% ~ 20% A 20 ~ 50% A- Others Very Good B+ < Random Guess C No submission F Top 3 students get one free cup of Bubble Tea! 6
7
Facebook Group (NTUT Deep RL Learning) 8
Teaching Assistants • 蔡榮成 : John Tsai (john0952270878@gmail.com) 9

Download Presentation

Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Deep Reinforcement Learning [Mastering the Game of Go with Deep Reinforcement Learning and Tree

Deep Reinforcement Learning [Mastering the Game of Go with Deep Reinforcement Learning and Tree Search, Nature 2016] CS 486/686 University of Waterloo Lecture 21: July 12, 2017 Outline AlphaGo Supervised Learning of Policy Networks

540 views • 15 slides

1 Deep Reinforcement Learning Qianqian Li, Nayeon Koong, Langtian He What is deep reinforcement

1 Deep Reinforcement Learning Qianqian Li, Nayeon Koong, Langtian He What is deep reinforcement learning? Agent/Actor + Action + Environment + State + Reward How does reinforcement learning work?

792 views • 31 slides

Deep Neural Networks and Deep Reinforcement Learning Deep Learning, Goodfellow, Bengio and

Deep Neural Networks and Deep Reinforcement Learning Deep Neural Networks and Deep Reinforcement Learning Deep Learning, Goodfellow, Bengio and Courville [chapt. 6,7,8]; AIMA [sect. 21.1-21.3]; Sutton and Barto, Reinforcement Learning: an

527 views • 35 slides

Deep Reinforcement Learning [Human-Level Control through deep reinforcement learning, Nature

Deep Reinforcement Learning [Human-Level Control through deep reinforcement learning, Nature 2015] CS 486/686 University of Waterloo Lecture 20: July 10, 2017 Outline Value Function Approximation Linear approximation Neural

706 views • 19 slides

Reproducibility and Replicability in Deep Reinforcement Learning (and Other Deep Learning

Reproducibility and Replicability in Deep Reinforcement Learning (and Other Deep Learning Methods) Peter Henderson Statistical Society of Canada Annual Meeting 2018 Contributors Riashat Islam Phil Bachman Doina Precup David Meger Joelle

850 views • 37 slides

Deep he(a)p, big feat arXiv:1707.06887 A Distributional Perspective on Reinforcement Learning

Deep he(a)p, big feat arXiv:1707.06887 A Distributional Perspective on Reinforcement Learning arXiv:1702.08165 Reinforcement Learning with Deep Energy-Based Policies 1 / 25 Reinforcement Learning Environment Action Reward Interpreter State

531 views • 25 slides

Reinforcement Learning CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1.

Introduction to Reinforcement Learning CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 1 is due next Wednesday! Remember that Monday is a holiday, so no office hours 2. Remember to start forming final project

877 views • 48 slides

Advanced Model-Based Reinforcement Learning CS 294-112: Deep Reinforcement Learning Sergey

Advanced Model-Based Reinforcement Learning CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 3 is extended by one week, to Wednesday after next Todays Lecture 1. Managing overfitting in model-based RL

686 views • 40 slides

Deep Reinforcement Learning and Complex Environments Raia Hadsell End-to-end Deep Learning

Deep Reinforcement Learning and Complex Environments Raia Hadsell End-to-end Deep Learning for robots? slide from V. Vanhoucke End-to-end Deep Learning for robots? 2010 : Speech Recognition Audio Acoustic Model Phonetic Model

1.05k views • 65 slides

Inverse Reinforcement Learning CS 294-112: Deep Reinforcement Learning Sergey Levine Todays

Inverse Reinforcement Learning CS 294-112: Deep Reinforcement Learning Sergey Levine Todays Lecture 1. So far: manually design reward function to define a task 2. What if we want to learn the reward function from observing an expert, and

650 views • 33 slides

CMP784 DEEP LEARNING Lecture #12 Deep Reinforcement Learning Aykut Erdem // Hacettepe

DeepLoco by X. B. Peng, G. Berseth & M. van de Panne CMP784 DEEP LEARNING Lecture #12 Deep Reinforcement Learning Aykut Erdem // Hacettepe University // Spring 2018 Neural Face by Taehoon Kim Previously on CMP784 Generative

895 views • 71 slides

Deep Reinforcement Learning for Robotics:

Deep Reinforcement Learning for Robotics: Frontiers and Beyond Shixiang (Shane) Gu ( ) 2018.5.27 01 Deep RL: successes and limitations Computation-Constrained Data-Constrained

634 views • 21 slides

Introduction to Deep Reinforcement Learning and Control Spring 2019, CMU 10-403 Katerina

Carnegie Mellon School of Computer Science Deep Reinforcement Learning and Control Introduction to Deep Reinforcement Learning and Control Spring 2019, CMU 10-403 Katerina Fragkiadaki Course Logistics Course website : all you need to

1.13k views • 88 slides

Reinforcement Learning Timothy Chou Charlie Tong Vincent Zhuang April 19, 2016 Reinforcement

Reinforcement Learning Q-Learning Deep Q-Learning on Atari Reinforcement Learning Timothy Chou Charlie Tong Vincent Zhuang April 19, 2016 Reinforcement Learning Q-Learning Deep Q-Learning on Atari Table of Contents Reinforcement Learning

938 views • 63 slides

Deep Reinforcement Learning 1 Outline 1. Overview of Reinforcement Learning 2. Policy Search 3.

Deep Reinforcement Learning 1 Outline 1. Overview of Reinforcement Learning 2. Policy Search 3. Policy Gradient and Gradient Estimators 4. Q-prop: Sample Efficient Policy Gradient and an Off-policy Critic 5. Model Based Planning in Discrete

769 views • 53 slides

CS885 Reinforcement Learning Module 2: June 6, 2020 Maximum Entropy Reinforcement Learning

CS885 Reinforcement Learning Module 2: June 6, 2020 Maximum Entropy Reinforcement Learning Haarnoja, Tang et al. (2017) Reinforcement Learning with Deep Energy Based Policies, ICML . Haarnoja, Zhou et al. (2018) Soft Actor-Critic: Off-Policy

682 views • 24 slides

CS885 Reinforcement Learning Lecture 1a: May 2, 2018 Course Introduction [SutBar] Chapter 1,

CS885 Reinforcement Learning Lecture 1a: May 2, 2018 Course Introduction [SutBar] Chapter 1, [Sze] Chapter 1 University of Waterloo CS885 Spring 2018 Pascal Poupart 1 Outline Introduction to Reinforcement Learning Course website and

424 views • 14 slides

$\ Task Scheduling in High-Performance Computing Thomas McSweeney School of Mathematics The$

\ Task Scheduling in High-Performance Computing Thomas McSweeney School of Mathematics The

\ Task Scheduling in High-Performance Computing Thomas McSweeney School of Mathematics The University of Manchester thomas.mcsweeney@postgrad.manchester.ac.uk Numerical Linear Algebra Group Meeting October 16, 2018 Outline 1 The task

315 views • 28 slides

SDRL: Interpretable and Data-efficient Deep Liu Reinforcement Learning Introduction Background

SDRL: Symbolic Deep Reinforcement Learning SDRL: Interpretable and Data-efficient Deep Liu Reinforcement Learning Introduction Background Leveraging Symbolic Planning Method Experiment Conclusion Bo Liu and Future Work Auburn

1k views • 20 slides

Reinforcement Learning: Basic models and algorithms Optimal decisions, Part VII Christos

Reinforcement Learning: Basic models and algorithms Optimal decisions, Part VII Christos Dimitrakakis Chalmers November 20, 2013 Christos Dimitrakakis (Chalmers) Reinforcement Learning: Basic models and algorithms November 20, 2013 1 / 28

405 views • 29 slides

55% Didactic Instruction Ongoing Training/PM 71% Lectures Monthly Feedback 1 10/6/2017

10/6/2017 Evidence Based Performance Management: Applying Behavioral Science to Support Practitioners Florence D. DiGennaro Reed, PhD, BCBA D Preservice Training 55% Didactic Instruction Ongoing Training/PM 71% Lectures Monthly Feedback

549 views • 29 slides

Presentation of WebCT usage in deploying quiz assignments Ivica.Matotek@CARNet.hr

Presentation of WebCT usage in deploying quiz assignments Ivica.Matotek@CARNet.hr Ivica.Matotek@CARNet.hr Edupoint Introduction WebCT (Web Courseware Tool) Developed at University of British Columbia, Canada, in 1995 Used in over 80

351 views • 20 slides

Mat MattNet tNet: : Modu Modular Atten lar Attention tion Network for Referring Network for

March 2020 Mat MattNet tNet: : Modu Modular Atten lar Attention tion Network for Referring Network for Referring Expres Expression Comp sion Comprehe rehension nsion Tong Gao Background Referring expressions are natural language

433 views • 26 slides

Synchronization CS 416: Operating Systems Design Department of Computer Science Rutgers

Synchronization CS 416: Operating Systems Design Department of Computer Science Rutgers University http://www.cs.rutgers.edu/~vinodg/teaching/416 Synchronization Problem Threads may share data Data consistency must be maintained Example

1.81k views • 99 slides