multi agent adversarial inverse reinforcement learning
play

Multi-Agent Adversarial Inverse Reinforcement Learning Lantao Yu, - PowerPoint PPT Presentation

May 11, 2023 •259 likes •511 views

Multi-Agent Adversarial Inverse Reinforcement Learning Lantao Yu, Jiaming Song, Stefano Ermon Department of Computer Science, Stanford University Contact: lantaoyu@cs.stanford.edu <latexit

  1. Multi-Agent Adversarial Inverse Reinforcement Learning Lantao Yu, Jiaming Song, Stefano Ermon Department of Computer Science, Stanford University Contact: lantaoyu@cs.stanford.edu

  2. <latexit sha1_base64="AKws76aOIQ/vlCa0QNvQLXdwHRA=">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</latexit> Motivation • By definition, the performance of RL agents heavily relies on the quality of reward functions. hP T i t =1 γ t r ( s t , a t ) max π E π Computer Games Multi-Agent System Dialogue • In many real-world scenarios, especially in multi-agent settings, hand-tuning informative reward functions can be very challenging. • Solution: learning from expert demonstrations!

  3. <latexit sha1_base64="mSOGy5SbNzZRqB+RiQ+OplYS+yA=">AB7XicbVBNSwMxEJ2tX7V+VT16CRahgpTdVrDHghePFewHtEvJptk2NpsSVYoS/+DFw+KePX/ePfmLZ70NYHA4/3ZpiZF8ScaeO6305uY3Nreye/W9jbPzg8Kh6ftLVMFKEtIrlU3QBrypmgLcMp91YURwFnHaCye3c7zxRpZkUD2YaUz/CI8FCRrCxUluV9RW+HBRLbsVdAK0TLyMlyNAcFL/6Q0mSiApDONa657mx8VOsDCOczgr9RNMYkwke0Z6lAkdU+ni2hm6sMoQhVLZEgYt1N8TKY60nkaB7YywGetVby7+5/USE9b9lIk4MVSQ5aIw4chINH8dDZmixPCpJZgoZm9FZIwVJsYGVLAheKsvr5N2teLVKtX761KjnsWRhzM4hzJ4cAMNuIMmtIDAIzDK7w50nlx3p2PZWvOyWZO4Q+czx+X0o5z</latexit> <latexit sha1_base64="oLJVDvbL3g/9lhUVNX+FRAp9xk0=">ACJXicbVDLSsNAFJ34rPVdelmsAjVRUmqoIsKBSm4rGAf0MQwmU7boZNJmJmIJeZn3PgrblxYRHDlrzhJu9DWAwNnzrmXe+/xQkalMs0vY2l5ZXVtPbeR39za3tkt7O23ZBAJTJo4YIHoeEgSRjlpKqoY6YSCIN9jpO2NrlO/UCEpAG/U+OQOD4acNqnGCktuYWqHdL7U3gFbR89urH+QZtyaDdokpq6HlxPckMt50bRYMoOYl9CRPHLdQNMtmBrhIrBkpghkabmFi9wIc+YQrzJCUXcsMlRMjoShmJMnbkSQhwiM0IF1NOfKJdOLsygQea6UH+4HQjyuYqb87YuRLOfY9XZkuLue9VPzP60aqf+nElIeRIhxPB/UjBlUA08hgjwqCFRtrgrCgeleIh0grHSweR2CNX/yImlVytZuXJ7XqxVZ3HkwCE4AiVgQtQAzegAZoAg2fwCt7BxHgx3owP43NaumTMeg7AHxjfP2AHpJQ=</latexit> <latexit sha1_base64="lADpjtdH09HXKxCgQmHRrYn7+4=">AB73icbVDLSgNBEOz1GeMr6tHLYBDiJexGwRwDXjxGMA9IltA7mSRDZmfXmVkhrPkJLx4U8ervePNvnCR70MSChqKqm+6uIBZcG9f9dtbWNza3tnM7+d29/YPDwtFxU0eJoqxBIxGpdoCaCS5Zw3AjWDtWDMNAsFYwvpn5rUemNI/kvZnEzA9xKPmAUzRWandjXsInfdErFN2yOwdZJV5GipCh3it8dfsRTUImDRWodcdzY+OnqAyngk3z3USzGOkYh6xjqcSQaT+d3zsl51bpk0GkbElD5urviRDrSdhYDtDNCO97M3E/7xOYgZVP+UyTgyTdLFokAhiIjJ7nvS5YtSIiSVIFbe3EjpChdTYiPI2BG/5VXSrJS9y3Ll7qpYq2Zx5OAUzqAEHlxDW6hDg2gIOAZXuHNeXBenHfnY9G65mQzJ/AHzucPiA2Pmg=</latexit> <latexit sha1_base64="dJnK71NR3o5vXieC5lAfNvyDvE=">AB8XicbVBNSwMxEJ2tX7V+VT16CRahXspuFeyxILHCvYD26Vk02wbmk2WJCuUtf/CiwdFvPpvPlvTNs9aOuDgcd7M8zMC2LOtHdbye3tr6xuZXfLuzs7u0fFA+PWlomitAmkVyqToA15UzQpmG06sKI4CTtvB+Hrmtx+p0kyKezOJqR/hoWAhI9hY6aEXs/5NGT/p836x5FbcOdAq8TJSgyNfvGrN5AkiagwhGOtu54bGz/FyjDC6bTQSzSNMRnjIe1aKnBEtZ/OL56iM6sMUCiVLWHQXP09keJI60kU2M4Im5Fe9mbif143MWHNT5mIE0MFWSwKE46MRLP30YApSgyfWIKJYvZWREZYWJsSAUbgrf8ipVSveRaV6d1mq17I48nACp1AGD6gDrfQgCYQEPAMr/DmaOfFeXc+Fq05J5s5hj9wPn8A0I+QUg=</latexit> Motivation • Imitation learning does not recover reward functions. • Behavior Cloning π ∗ = max π ∈ Π E π E [log π ( a | s )] • Generative Adversarial Imitation Learning [Ho & Ermon, 2016] IRL RL π ( a | s ) π E ( a | s ) r ( s, a )

  4. <latexit sha1_base64="lADpjtdH09HXKxCgQmHRrYn7+4=">AB73icbVDLSgNBEOz1GeMr6tHLYBDiJexGwRwDXjxGMA9IltA7mSRDZmfXmVkhrPkJLx4U8ervePNvnCR70MSChqKqm+6uIBZcG9f9dtbWNza3tnM7+d29/YPDwtFxU0eJoqxBIxGpdoCaCS5Zw3AjWDtWDMNAsFYwvpn5rUemNI/kvZnEzA9xKPmAUzRWandjXsInfdErFN2yOwdZJV5GipCh3it8dfsRTUImDRWodcdzY+OnqAyngk3z3USzGOkYh6xjqcSQaT+d3zsl51bpk0GkbElD5urviRDrSdhYDtDNCO97M3E/7xOYgZVP+UyTgyTdLFokAhiIjJ7nvS5YtSIiSVIFbe3EjpChdTYiPI2BG/5VXSrJS9y3Ll7qpYq2Zx5OAUzqAEHlxDW6hDg2gIOAZXuHNeXBenHfnY9G65mQzJ/AHzucPiA2Pmg=</latexit> <latexit sha1_base64="oLJVDvbL3g/9lhUVNX+FRAp9xk0=">ACJXicbVDLSsNAFJ34rPVdelmsAjVRUmqoIsKBSm4rGAf0MQwmU7boZNJmJmIJeZn3PgrblxYRHDlrzhJu9DWAwNnzrmXe+/xQkalMs0vY2l5ZXVtPbeR39za3tkt7O23ZBAJTJo4YIHoeEgSRjlpKqoY6YSCIN9jpO2NrlO/UCEpAG/U+OQOD4acNqnGCktuYWqHdL7U3gFbR89urH+QZtyaDdokpq6HlxPckMt50bRYMoOYl9CRPHLdQNMtmBrhIrBkpghkabmFi9wIc+YQrzJCUXcsMlRMjoShmJMnbkSQhwiM0IF1NOfKJdOLsygQea6UH+4HQjyuYqb87YuRLOfY9XZkuLue9VPzP60aqf+nElIeRIhxPB/UjBlUA08hgjwqCFRtrgrCgeleIh0grHSweR2CNX/yImlVytZuXJ7XqxVZ3HkwCE4AiVgQtQAzegAZoAg2fwCt7BxHgx3owP43NaumTMeg7AHxjfP2AHpJQ=</latexit> <latexit sha1_base64="dJnK71NR3o5vXieC5lAfNvyDvE=">AB8XicbVBNSwMxEJ2tX7V+VT16CRahXspuFeyxILHCvYD26Vk02wbmk2WJCuUtf/CiwdFvPpvPlvTNs9aOuDgcd7M8zMC2LOtHdbye3tr6xuZXfLuzs7u0fFA+PWlomitAmkVyqToA15UzQpmG06sKI4CTtvB+Hrmtx+p0kyKezOJqR/hoWAhI9hY6aEXs/5NGT/p836x5FbcOdAq8TJSgyNfvGrN5AkiagwhGOtu54bGz/FyjDC6bTQSzSNMRnjIe1aKnBEtZ/OL56iM6sMUCiVLWHQXP09keJI60kU2M4Im5Fe9mbif143MWHNT5mIE0MFWSwKE46MRLP30YApSgyfWIKJYvZWREZYWJsSAUbgrf8ipVSveRaV6d1mq17I48nACp1AGD6gDrfQgCYQEPAMr/DmaOfFeXc+Fq05J5s5hj9wPn8A0I+QUg=</latexit> <latexit sha1_base64="3q69riKgMJ7dCLeUS7TDplOABc=">AB7XicbVBNSwMxEJ2tX7V+VT16CRahgpTdVrDHghePFewHtEvJptk2NpsSVYoS/+DFw+KePX/ePfmLZ70NYHA4/3ZpiZF8ScaeO6305uY3Nreye/W9jbPzg8Kh6ftLVMFKEtIrlU3QBrypmgLcMp91YURwFnHaCye3c7zxRpZkUD2YaUz/CI8FCRrCxUjsu6yt8OSiW3Iq7AFonXkZKkKE5KH71h5IkERWGcKx1z3Nj46dYGUY4nRX6iaYxJhM8oj1LBY6o9tPFtTN0YZUhCqWyJQxaqL8nUhxpPY0C2xlhM9ar3lz8z+slJqz7KRNxYqgy0VhwpGRaP46GjJFieFTSzBRzN6KyBgrTIwNqGBD8FZfXiftasWrVar316VGPYsjD2dwDmXw4AYacAdNaAGBR3iGV3hzpPivDsfy9ack82cwh84nz+UwI5x</latexit> Motivation • Imitation learning does not recover reward functions. • Behavior Cloning π ∗ = max π ∈ Π E π E [log π ( a | s )] • Generative Adversarial Imitation Learning [Ho & Ermon, 2016] π ( a | s ) π E ( a | s ) Matching with GAN p ( s, a )

  5. <latexit sha1_base64="yr7Og+EdZ1l80MCEkGrNsiJiA=">ACGHicbZDLSgMxFIYzXmu9V26CRahCtaZKtiNUHDjsoK9QC9DJs20sZnJkJwRy9DHcOruHGhiNvufBvTy0Jbfwj8fOcTs7vRYJrsO1va2l5ZXVtPbWR3tza3tnN7O1XtYwVZRUqhVR1j2gmeMgqwEGweqQYCTzBal7/ZlyvPTKluQzvYRCxVkC6Ifc5JWCQmzlX7VN8jXNYE+QSO+BUWi6kdRDfIansCuJmKTdsHNZO28PRFeNM7MZNFMZTczanYkjQMWAhVE64ZjR9BKiAJOBRum7FmEaF90mUNY0MSMN1KJocN8bEhHexLZV4IeEJ/TyQk0HoQeKYzINDT87Ux/K/WiMEvthIeRjGwkE4X+bHAIPE4JdzhygQhBsYQqrj5K6Y9ogFk2XahODMn7xoqoW8c5Ev3F1mS8VZHCl0iI5QDjnoCpXQLSqjCqLoGb2id/RhvVhv1qf1NW1dsmYzB+iPrNEP2UGfpg=</latexit> <latexit sha1_base64="iMdxnKiMTtlEX/9UuknRpqdtNU=">ACGHicbVC7TsMwFHXKq5RXgJHFokIqDCUpCBTEQtjEfQhNaFyXLe16jiR7SBVUT6DhV9hYQAh1m78DU4b8SgcydLxOfq3nu8kFGpLOvDyM3NLywu5ZcLK6tr6xvm5lZDBpHApI4DFoiWhyRhlJO6oqRVigI8j1Gmt7wMvWb90RIGvBbNQqJ6M+pz2KkdJSxzx0Qnp3cA4dH6kBRiy+SaCjgu9/LSl98Ytkv2MWrbI1AfxL7IwUQYZaxw73QBHPuEKMyRl27ZC5cZIKIoZSQpOJEmI8BD1SVtTjnwi3XhyWAL3tNKFvUDoxWcqD87YuRLOfI9XZnuKGe9VPzPa0eqd+bGlIeRIhxPB/UiBvXlaUqwSwXBio0QVhQvSvEAyQVjrLg7Bnj35L2lUyvZRuXJ9XKyeZHkwQ7YBSVg1NQBVegBuoAgwfwBF7Aq/FoPBtvxvu0NGdkPdvgF4zxJyEOn84=</latexit> Motivation • Why should we care reward learning? • Scientific inquiry: human and animal behavioral study, inferring intentions, etc. • Presupposition: reward function is considered to be the most succinct, robust and transferable description of the task. [Abbeel & Ng, 2014] r ∗ = (object pos − goal pos) 2 VS. π ∗ : S → P ( A ) • Re-optimizing policies in new environments, debugging and analyzing imitation learning algorithms, etc. • These properties are even more desirable in the multi-agent settings.

  6. <latexit sha1_base64="hVwuR8Ted1t3O6ZK0IXne5ndZM=">AB73icbVDLSgNBEJz1GeMr6tHLYBA8hd0omGPAi8cI5oHJEmYnvcmQeawzs0JY8hNePCji1d/x5t84SfagiQUNRVU3V1Rwpmxv/tra1vbG5tF3aKu3v7B4elo+OWUam0KSK92JiAHOJDQtsxw6iQYiIg7taHwz89tPoA1T8t5OEgFGUoWM0qskzoP/Z4SMCT9Utmv+HPgVRLkpIxyNPqlr95A0VSAtJQTY7qBn9gwI9oymFa7KUGEkLHZAhdRyURYMJsfu8UnztlgGOlXUmL5+rviYwIYyYicp2C2JFZ9mbif143tXEtzJhMUguSLhbFKcdW4dnzeMA0UMsnjhCqmbsV0xHRhFoXUdGFECy/vEpa1UpwWaneXZXrtTyOAjpFZ+gCBega1dEtaqAmoijZ/SK3rxH78V79z4WrWtePnOC/sD7/AH1fo/i</latexit> <latexit sha1_base64="BmMsp83qz7lP2nj6Yhkur/PhWg0=">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</latexit> Preliminaries • Single-Agent Inverse RL • Basic principle: find a reward function that explains the expert behaviors. (ill-defined) • Maximum Entropy Inverse RL (MaxEnt IRL) provides a general probabilistic framework to solve the ambiguity. • Maximum Entropy Inverse RL (MaxEnt IRL) provides a general probabilistic framework to solve the ambiguity. " T # T ! Y X η ( s 1 ) P ( s t +1 | s t , a t ) r ω ( s t , a t ) p ω ( τ ) ∝ exp t =1 t =1 " T # X r ω ( s t , a t ) max E π E [log p ω ( τ )] = E τ ∼ π E − log Z ω ω t =1 where is the partition function. Z ω

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

Multi-agent learning Multi-agent reinforcement learning Gerard Vreeswijk , Intelligent Systems

Multi-agent learning Multi-agent reinforcement learning Gerard Vreeswijk , Intelligent Systems

Multi-agent learning Multi-agent reinforcement learning Multi-agent learning Multi-agent reinforcement learning Gerard Vreeswijk , Intelligent Systems Group, Computer Science Department, Faculty of Sciences, Utrecht University, The Netherlands.

752 views • 21 slides
1. Algorithms for Inverse Reinforcement Learning 2. Apprenticeship learning via Inverse

1. Algorithms for Inverse Reinforcement Learning 2. Apprenticeship learning via Inverse

1. Algorithms for Inverse Reinforcement Learning 2. Apprenticeship learning via Inverse Reinforcement Learning Algorithms for Inverse Reinforcement Learning Andrew Ng and Stuart Russell Motivation Given: (1) measurements of an agent's

379 views • 34 slides
Maximum Entropy Inverse RL, Adversarial imitation learning Katerina Fragkiadaki Reinforcement

Maximum Entropy Inverse RL, Adversarial imitation learning Katerina Fragkiadaki Reinforcement

Carnegie Mellon School of Computer Science Deep Reinforcement Learning and Control Maximum Entropy Inverse RL, Adversarial imitation learning Katerina Fragkiadaki Reinforcement Learning Probability Dynamics distribution over next Model T

911 views • 66 slides
Maximum Entropy Inverse RL, Adversarial imitation learning Katerina Fragkiadaki Reinforcement

Maximum Entropy Inverse RL, Adversarial imitation learning Katerina Fragkiadaki Reinforcement

Carnegie Mellon School of Computer Science Deep Reinforcement Learning and Control Maximum Entropy Inverse RL, Adversarial imitation learning Katerina Fragkiadaki Reinforcement Learning Probability Dynamics distribution over next Model T

608 views • 56 slides
REINFORCEMENT LEARNING IN MULTI-AGENT SYSTEMS MACHINE LEARNING MEETUP DR. ANA PELETEIRO

REINFORCEMENT LEARNING IN MULTI-AGENT SYSTEMS MACHINE LEARNING MEETUP DR. ANA PELETEIRO

REINFORCEMENT LEARNING IN MULTI-AGENT SYSTEMS MACHINE LEARNING MEETUP DR. ANA PELETEIRO RAMALLO 29-08-2016 TABLE OF CONTENTS MULTI-AGENT SYSTEMS GAME THEORY REINFORCEMENT LEARNING MULTI-AGENT LEARNING 2 ZALANDO Our purpose: to Zalando

1.45k views • 20 slides
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning Kyunghwan Son , Daewoo Kim, Wan Ju Kang, David Hostallero, Yung Yi School of Electrical Engineering, KAIST Cooperative Multi-Agent Reinforcement

662 views • 8 slides
LAB MEETING: A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning

LAB MEETING: A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning

LAB MEETING: A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning and Energy-Based models Suwon Suh POSTECH MLG Feb, 13, 2017 Goal Understanding Basic Models 1) Generative Adversarial Networks (GAN) 2)

722 views • 12 slides
ROMA: Multi-Agent Reinforcement Learning with Emerging Roles Tonghan Wang, Heng Dong, Victor

ROMA: Multi-Agent Reinforcement Learning with Emerging Roles Tonghan Wang, Heng Dong, Victor

ROMA: Multi-Agent Reinforcement Learning with Emerging Roles Tonghan Wang, Heng Dong, Victor Lesser, Chongjie Zhang Tsinghua University, UMass Amherst Multi-Agent Systems Robot Football Game Multi-Agent Assembly One Major Challenge of

426 views • 30 slides
YunQi 2050 - DRL Session Communication in Multi-agent Reinforcement Learning Ying Wen

YunQi 2050 - DRL Session Communication in Multi-agent Reinforcement Learning Ying Wen

YunQi 2050 - DRL Session Communication in Multi-agent Reinforcement Learning Ying Wen Department of Computer Science, University College London MediaGamma Ltd. ying.wen@cs.ucl.ac.uk 30 May, 2018 Multi-agent in Real-World Human

402 views • 27 slides
Multi-agent learning Gerard Vreeswijk , Intelligent Systems Group, Computer Science Department,

Multi-agent learning Gerard Vreeswijk , Intelligent Systems Group, Computer Science Department,

Multi-agent learning Multi-agent reinforcement learning Multi-agent reinfo rement lea rning Multi-agent learning Gerard Vreeswijk , Intelligent Systems Group, Computer Science Department, Faculty of Sciences, Utrecht University, The

528 views • 21 slides
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations Daniel Brown*, Wonjoon Goo*, Prabhat Nagarajan, and Scott Niekum Inverse Reinforcement Learning Current approaches 1. Cant do better

289 views • 27 slides
Multi-armed bandit problem and its applications in reinforcement learning Pietro Lovato Ph.D.

Multi-armed bandit problem and its applications in reinforcement learning Pietro Lovato Ph.D.

University of Verona 28/01/2013 Multi-armed bandit problem and its applications in reinforcement learning Pietro Lovato Ph.D. Course on Special Topics in AI: Intelligent Agents and Multi-Agent Systems Overview Introduction: Reinforcement

628 views • 25 slides
Decentralized Stochastic Approximation, Optimization, and Multi-Agent Reinforcement Learning

Decentralized Stochastic Approximation, Optimization, and Multi-Agent Reinforcement Learning

Decentralized Stochastic Approximation, Optimization, and Multi-Agent Reinforcement Learning Justin Romberg, Georgia Tech ECE CAMDA/TAMIDS Seminar, Texas A&amp; M College Station, Texas Streaming live from Atlanta, Georgia March 16, 2020

689 views • 64 slides
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI Lei Han* 1 , Peng Sun* 1

Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI Lei Han* 1 , Peng Sun* 1

Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI Lei Han* 1 , Peng Sun* 1 , Yali Du* 2 , Jiechao Xiong 1 , Qing Wang 1 , Xinghai Sun 1 , Han Liu 3 , Tong Zhang 4 1 Tencent AI Lab, Shenzhen, China 2 University of

358 views • 8 slides
TextMed: A Multi-Agent System with Reinforcement Learning Agents for Biomedical Text Mining

TextMed: A Multi-Agent System with Reinforcement Learning Agents for Biomedical Text Mining

TextMed: A Multi-Agent System with Reinforcement Learning Agents for Biomedical Text Mining Michael Camara Janyl Jumadinova Oliver Bonham-Carter September 9, 2015 Big Data Biomedical Research PubMed: U.S. National Library of Medicine

346 views • 31 slides
Decentralized Non- Communicating Multi-agent Collision Avoidance with Deep Reinforcement

Decentralized Non- Communicating Multi-agent Collision Avoidance with Deep Reinforcement

Decentralized Non- Communicating Multi-agent Collision Avoidance with Deep Reinforcement Learning By Yu Fan Chen, Miao Liu, Michael Everett, and Jonathan P . How Presenter: Jared Choi Motivation Finding a path Computationally

808 views • 44 slides
Toward a Common Model for Highly Concurrent Applications Douglas Thain University of Notre Dame

Toward a Common Model for Highly Concurrent Applications Douglas Thain University of Notre Dame

Toward a Common Model for Highly Concurrent Applications Douglas Thain University of Notre Dame MTAGS Workshop 17 November 2013 Overview Experience with Concurrent Applications Makeflow, Weaver, Work Queue Thesis: Convergence of

826 views • 56 slides
Authen'ca'on CS461/ECE422 Spring 2012 Readings Chapter 3

Authen'ca'on CS461/ECE422 Spring 2012 Readings Chapter 3

Authen'ca'on CS461/ECE422 Spring 2012 Readings Chapter 3 from text Rainbow tables hCp://kestas.kuliukas.com/RainbowTables/ Chapter 10 from Handbook of

536 views • 41 slides
Course Outline Introduction and the MPEG standards Introduction to statistical pattern

Course Outline Introduction and the MPEG standards Introduction to statistical pattern

E E E E 810 8108 Multime Multimedia ia Proce Processing ssing &amp; &amp; Commu Communicat nications Course Instructor: Pro f. L ing Guan Department of Electrical &amp; Computer Engineering Room 315, ENG Building Tel:

854 views • 30 slides
The AXIOM-board: bringing programmability, acceleration, scalability into a 64-bit hand-size board

The AXIOM-board: bringing programmability, acceleration, scalability into a 64-bit hand-size board

Agile, eXtensible, fast I/O Module for the cyber-physical era IWES 2017 2 nd Italian Workshop on Embedded Systems Rome, 7-8 September 2017 The AXIOM-board: bringing programmability, acceleration, scalability into a 64-bit hand-size board

866 views • 32 slides
Reinforcement Learning Ziebart, Maas, Bagnell, Dey Presenter: Naireen Hussain Overview What

Reinforcement Learning Ziebart, Maas, Bagnell, Dey Presenter: Naireen Hussain Overview What

Maximum Entropy Inverse Reinforcement Learning Ziebart, Maas, Bagnell, Dey Presenter: Naireen Hussain Overview What is Inverse Reinforcement Learning (IRL)? What are the diffjcultjes with IRL? Researchers Contributjons

407 views • 22 slides
Gen enerativ erative e Adver ersaria sarial l Im Imitation itation Le Learning arning

Gen enerativ erative e Adver ersaria sarial l Im Imitation itation Le Learning arning

Gen enerativ erative e Adver ersaria sarial l Im Imitation itation Le Learning arning Stefan efano o Ermon on Joint work with Jayesh Gupta, Jonathan Ho, Yunzhu Li, Hongyu Ren, and Jiaming Song Reinforcement Learning Goal: Learn

530 views • 40 slides
Networks Used As Episodic Memory for An Autonomous Robot Outline

Networks Used As Episodic Memory for An Autonomous Robot Outline

Fusion Adaptive Resonance Theory Networks Used As Episodic Memory for An Autonomous Robot Outline 2 Goals for autonomous robots IRL-1 3 Role of episodic memory 4 Proposed model: EM-ART 5

392 views • 22 slides
NAG : Motivating Deployment of Networked Systems Mohit Lad UCLA Deployment of Networked Systems

NAG : Motivating Deployment of Networked Systems Mohit Lad UCLA Deployment of Networked Systems

NAG : Motivating Deployment of Networked Systems Mohit Lad UCLA Deployment of Networked Systems Goal: How do you convince users to deploy applications? Prior work: Lottery System (Sigcomm 07) Probabilistic incentive to motivate

448 views • 7 slides

More recommend