TalkRL: The Reinforcement Learning podcast

#Reinforcement Learning #Machine Learning #Robin Ranjit Singh Chauhan #Artificial Intelligence #Tech

1:00:36

Do you have fond childhood memories of summer camp? For a chance at $250,000, campers must compete in a series of summer camp-themed challenges to prove that they are unbeatable, unhateable, and unbreakable. Host Chris Burns is joined by the multi-talented comedian Dana Moon to recap the first five episodes of season one of Battle Camp . Plus, Quori-Tyler (aka QT) joins the podcast to dish on the camp gossip, team dynamics, and the Watson to her Sherlock Holmes. Leave us a voice message at www.speakpipe.com/WeHaveTheReceipts Text us at (929) 487-3621 DM Chris @FatCarrieBradshaw on Instagram Follow We Have The Receipts wherever you listen, so you never miss an episode. Listen to more from Netflix Podcasts.…

TalkRL: The Reinforcement Learning Podcast

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TalkRL podcast is All Reinforcement Learning, All the Time. In-depth interviews with brilliant people at the forefront of RL research and practice. Guests from places like MILA, OpenAI, MIT, DeepMind, Berkeley, Amii, Oxford, Google Research, Brown, Waymo, Caltech, and Vector Institute. Hosted by Robin Ranjit Singh Chauhan.

66 episodes

TalkRL: The Reinforcement Learning Podcast

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maison des série•Feed

66 episodes

#Reinforcement Learning #Machine Learning #Robin Ranjit Singh Chauhan #Artificial Intelligence #Tech

All episodes

1
NeurIPS 2024 - Posters and Hallways 3 10:01

il y a 10 weeks10:01

10:01

Posters and Hallway episodes are short interviews and poster summaries. Recorded at NeurIPS 2024 in Vancouver BC Canada. Featuring Claire Bizon Monroc from Inria: WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control Andrew Wagenmaker from UC Berkeley: Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL Harley Wiltzer from MILA: Foundations of Multivariate Distributional Reinforcement Learning Vinzenz Thoma from ETH AI Center: Contextual Bilevel Reinforcement Learning for Incentive Alignment Haozhe (Tony) Chen & Ang (Leon) Li from Columbia: QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers…

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NeurIPS 2024 - Posters and Hallways 2 8:48

il y a 11 weeks8:48

8:48

Posters and Hallway episodes are short interviews and poster summaries. Recorded at NeurIPS 2024 in Vancouver BC Canada. Featuring Jonathan Cook from University of Oxford: Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning Yifei Zhou from Berkeley AI Research: DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning Rory Young from University of Glasgow: Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach Glen Berseth from MILA: Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn Alexander Rutherford from University of Oxford: JaxMARL: Multi-Agent RL Environments and Algorithms in JAX…

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NeurIPS 2024 - Posters and Hallways 1 9:32

il y a 11 weeks9:32

9:32

Posters and Hallway episodes are short interviews and poster summaries. Recorded at NeurIPS 2024 in Vancouver BC Canada. Featuring Jiaheng Hu of University of Texas: Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning Skander Moalla of EPFL: No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO Adil Zouitine of IRT Saint Exupery/Hugging Face : Time-Constrained Robust MDPs Soumyendu Sarkar of HP Labs : SustainDC: Benchmarking for Sustainable Data Center Control Matteo Bettini of Cambridge University: BenchMARL: Benchmarking Multi-Agent Reinforcement Learning Michael Bowling of U Alberta : Beyond Optimism: Exploration With Partially Observable Rewards…

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Abhishek Naik on Continuing RL & Average Reward 1:21:40

il y a 14 weeks1:21:40

1:21:40

Abhishek Naik was a student at University of Alberta and Alberta Machine Intelligence Institute, and he just finished his PhD in reinforcement learning, working with Rich Sutton. Now he is a postdoc fellow at the National Research Council of Canada, where he does AI research on Space applications. Featured References Reinforcement Learning for Continuing Problems Using Average Reward Abhishek Naik Ph.D. dissertation 2024 Reward Centering Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton 2024 Learning and Planning in Average-Reward Markov Decision Processes Yi Wan, Abhishek Naik, Richard S. Sutton 2020 Discounted Reinforcement Learning Is Not an Optimization Problem Abhishek Naik, Roshan Shariff, Niko Yasui, Hengshuai Yao, Richard S. Sutton 2019 Additional References Explaining dopamine through prediction errors and beyond , Gershman et al 2024 (proposes Differential-TD-like learning mechanism in the brain around Box 4)…

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Neurips 2024 RL meetup Hot takes: What sucks about RL? 17:45

il y a 21 weeks17:45

17:45

What do RL researchers complain about after hours at the bar? In this "Hot takes" episode, we find out! Recorded at The Pearl in downtown Vancouver, during the RL meetup after a day of Neurips 2024. Special thanks to "David Beckham" for the inspiration :)

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RLC 2024 - Posters and Hallways 5 13:17

il y a 34 weeks13:17

13:17

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 David Radke of the Chicago Blackhawks NHL on RL for professional sports 0:56 Abhishek Naik from the National Research Council on Continuing RL and Average Reward 2:42 Daphne Cornelisse from NYU on Autonomous Driving and Multi-Agent RL 08:58 Shray Bansal from Georgia Tech on Cognitive Bias for Human AI Ad hoc Teamwork 10:21 Claas Voelcker from University of Toronto on Can we hop in general? 11:23 Brent Venable from The Institute for Human & Machine Cognition on Cooperative information dissemination…

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RLC 2024 - Posters and Hallways 4 4:52

il y a 34 weeks4:52

4:52

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 David Abel from DeepMind on 3 Dogmas of RL 0:55 Kevin Wang from Brown on learning variable depth search for MCTS 2:17 Ashwin Kumar from Washington University in St Louis on fairness in resource allocation 3:36 Prabhat Nagarajan from UAlberta on Value overestimation…

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RLC 2024 - Posters and Hallways 3 6:43

il y a 34 weeks6:43

6:43

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 Kris De Asis from Openmind on Time Discretization 2:23 Anna Hakhverdyan from U of Alberta on Online Hyperparameters 3:59 Dilip Arumugam from Princeton on Information Theory and Exploration 5:04 Micah Carroll from UC Berkeley on Changing preferences and AI alignment…

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RLC 2024 - Posters and Hallways 2 15:52

il y a 35 weeks15:52

15:52

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 Hector Kohler from Centre Inria de l'Université de Lille with " Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning " 2:29 Quentin Delfosse from TU Darmstadt on " Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents " 4:15 Sonja Johnson-Yu from Harvard on " Understanding biological active sensing behaviors by interpreting learned artificial agent policies " 6:42 Jannis Blüml from TU Darmstadt on " OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments " 8:20 Cameron Allen from UC Berkeley on " Resolving Partial Observability in Decision Processes via the Lambda Discrepancy " 9:48 James Staley from Tufts on " Agent-Centric Human Demonstrations Train World Models " 14:54 Jonathan Li from Rensselaer Polytechnic Institute…

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RLC 2024 - Posters and Hallways 1 5:46

il y a 36 weeks5:46

5:46

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 Ann Huang from Harvard on Learning Dynamics and the Geometry of Neural Dynamics in Recurrent Neural Controllers 1:37 Jannis Blüml from TU Darmstadt on HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning 3:13 Benjamin Fuhrer from NVIDIA on Gradient Boosting Reinforcement Learning 3:54 Paul Festor from Imperial College London on Evaluating the impact of explainable RL on physician decision-making in high-fidelity simulations: insights from eye-tracking metrics…

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Finale Doshi-Velez on RL for Healthcare @ RCL 2024 7:35

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Finale Doshi-Velez is a Professor at the Harvard Paulson School of Engineering and Applied Sciences. This off-the-cuff interview was recorded at UMass Amherst during the workshop day of RL Conference on August 9th 2024. Host notes: I've been a fan of some of Prof Doshi-Velez' past work on clinical RL and hoped to feature her for some time now, so I jumped at the chance to get a few minutes of her thoughts -- even though you can tell I was not prepared and a bit flustered tbh. Thanks to Prof Doshi-Velez for taking a moment for this, and I hope to cross paths in future for a more in depth interview. References Finale Doshi-Velez Homepage @ Harvard Finale Doshi-Velez on Google Scholar…

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David Silver 2 - Discussion after Keynote @ RCL 2024 16:17

il y a 37 weeks16:17

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Thanks to Professor Silver for permission to record this discussion after his RLC 2024 keynote lecture. Recorded at UMass Amherst during RCL 2024. Due to the live recording environment, audio quality varies. We publish this audio in its raw form to preserve the authenticity and immediacy of the discussion. References AlphaProof announcement on DeepMind's blog Discovering Reinforcement Learning Algorithms , Oh et al -- His keynote at RLC 2024 referred to more recent update to this work, yet to be published Reinforcement Learning Conference 2024 David Silver on Google Scholar…

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David Silver @ RCL 2024 11:27

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David Silver is a principal research scientist at DeepMind and a professor at University College London. This interview was recorded at UMass Amherst during RLC 2024. References Discovering Reinforcement Learning Algorithms , Oh et al -- His keynote at RLC 2024 referred to more recent update to this work, yet to be published Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm , Silver et al 2017 -- the AlphaZero algo was used in his recent work on AlphaProof AlphaProof on the DeepMind blog AlphaFold on the DeepMind blog Reinforcement Learning Conference 2024 David Silver on Google Scholar…

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Vincent Moens on TorchRL 40:14

il y a 1 year40:14

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Dr. Vincent Moens is an Applied Machine Learning Research Scientist at Meta, and an author of TorchRL and TensorDict in pytorch. Featured References TorchRL: A data-driven decision-making library for PyTorch Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, Vincent Moens Additional References TorchRL on github TensorDict Documentation…

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Arash Ahmadian on Rethinking RLHF 33:30

il y a 1 year33:30

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Arash Ahmadian is a Researcher at Cohere and Cohere For AI focussed on Preference Training of large language models. He’s also a researcher at the Vector Institute of AI. Featured Reference Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, Sara Hooker Additional References Self-Rewarding Language Models , Yuan et al 2024 Reinforcement Learning: An Introduction , Sutton and Barto 1992 Learning from Delayed Rewards , Chris Watkins 1989 Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , Williams 1992…

TalkRL: The Reinforcement Learning Podcast

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Sai Krishna Gottipati 1:08:11

il y a 3 years1:08:11

1:08:11

Saikrishna Gottipati is an RL Researcher at AI Redefined, working on RL, MARL, human in the loop learning. Featured References Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations AI Redefined, Sai Krishna Gottipati, Sagar Kurandwad, Clodéric Mars, Gregory Szriftgiser, François Chabot Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning Currently under review Learning to navigate the synthetically accessible chemical space using reinforcement learning Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio Additional References Asymmetric self-play for automatic goal discovery in robotic manipulation , 2021 OpenAI et al Continuous Coordination As a Realistic Scenario for Lifelong Learning , 2021 Nekoei et al Episode sponsor: Anyscale Ray Summit 2022 is coming to San Francisco on August 23-24. Hear how teams at Dow, Verizon, Riot Games, and more are solving their RL challenges with Ray's RLlib. Register at raysummit.org and use code RAYSUMMIT22RL for a further 25% off the already reduced prices.…

TalkRL: The Reinforcement Learning Podcast

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Aravind Srinivas 2 58:33

il y a 3 years58:33

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Aravind Srinivas is back! He is now a research Scientist at OpenAI. Featured References Decision Transformer: Reinforcement Learning via Sequence Modeling Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch VideoGPT: Video Generation using VQ-VAE and Transformers Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas…

TalkRL: The Reinforcement Learning Podcast

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Rohin Shah 1:37:04

il y a 3 years1:37:04

1:37:04

Dr. Rohin Shah is a Research Scientist at DeepMind, and the editor and main contributor of the Alignment Newsletter. Featured References The MineRL BASALT Competition on Learning from Human Feedback Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William Guss, Sharada Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca Dragan Preferences Implicit in the State of the World Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca Dragan Benefits of Assistance over Reward Learning Rohin Shah, Pedro Freire, Neel Alex, Rachel Freedman, Dmitrii Krasheninnikov, Lawrence Chan, Michael D Dennis, Pieter Abbeel, Anca Dragan, Stuart Russell On the Utility of Learning about Humans for Human-AI Coordination Micah Carroll, Rohin Shah, Mark K. Ho, Thomas L. Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca Dragan Evaluating the Robustness of Collaborative Agents Paul Knott, Micah Carroll, Sam Devlin, Kamil Ciosek, Katja Hofmann, A. D. Dragan, Rohin Shah Additional References AGI Safety Fundamentals , EA Cambridge…

TalkRL: The Reinforcement Learning Podcast

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Jordan Terry 1:03:48

il y a 3 years1:03:48

1:03:48

Jordan Terry is a PhD candidate at University of Maryland, the maintainer of Gym, the maintainer and creator of PettingZoo and the founder of Swarm Labs. Featured References PettingZoo: Gym for Multi-Agent Reinforcement Learning J. K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis Santos, Rodrigo Perez, Caroline Horsch, Clemens Dieffendahl, Niall L. Williams, Yashas Lokesh, Praveen Ravi PettingZoo on Github gym on Github Additional References Time Limits in Reinforcement Learning , Pardo et al 2017 Deep Reinforcement Learning at the Edge of the Statistical Precipice , Agarwal et al 2021…

TalkRL: The Reinforcement Learning Podcast

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Robert Lange 1:10:57

il y a 3 years1:10:57

1:10:57

Robert Tjarko Lange is a PhD student working at the Technical University Berlin. Featured References Learning not to learn: Nature versus nurture in silico Lange, R. T., & Sprekeler, H. (2020) On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning Vischer, M. A., Lange, R. T., & Sprekeler, H. (2021). Semantic RL with Action Grammars: Data-Efficient Learning of Hierarchical Task Abstractions Lange, R. T., & Faisal, A. (2019). MLE-Infrastructure on Github Additional References RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning , Duan et al 2016 Learning to reinforcement learn , Wang et al 2016 Decision Transformer: Reinforcement Learning via Sequence Modeling , Chen et al 2021…

TalkRL: The Reinforcement Learning Podcast

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NeurIPS 2021 Political Economy of Reinforcement Learning Systems (PERLS) Workshop 24:07

il y a 3 years24:07

24:07

We hear about the idea of PERLS and why its important to talk about. Political Economy of Reinforcement Learning (PERLS) Workshop at NeurIPS 2021 on Tues Dec 14th NeurIPS 2021

TalkRL: The Reinforcement Learning Podcast

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Amy Zhang 1:09:35

il y a 4 years1:09:35

1:09:35

Amy Zhang is a postdoctoral scholar at UC Berkeley and a research scientist at Facebook AI Research. She will be starting as an assistant professor at UT Austin in Spring 2023. Featured References Invariant Causal Prediction for Block MDPs Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup Multi-Task Reinforcement Learning with Context-based Representations Shagun Sodhani, Amy Zhang, Joelle Pineau MBRL-Lib: A Modular Library for Model-based Reinforcement Learning Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra Additional References Amy Zhang - Exploring Context for Better Generalization in Reinforcement Learning @ UCL DARK ICML 2020 Poster session: Invariant Causal Prediction for Block MDPs Clare Lyle - Invariant Prediction for Generalization in Reinforcement Learning @ Simons Institute…

TalkRL: The Reinforcement Learning Podcast

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Xianyuan Zhan 41:30

il y a 4 years41:30

41:30

Xianyuan Zhan is currently a research assistant professor at the Institute for AI Industry Research (AIR), Tsinghua University. He received his Ph.D. degree at Purdue University. Before joining Tsinghua University, Dr. Zhan worked as a researcher at Microsoft Research Asia (MSRA) and a data scientist at JD Technology. At JD Technology, he led the research that uses offline RL to optimize real-world industrial systems. Featured References DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning Xianyuan Zhan, Haoran Xu, Yue Zhang, Yusen Huo, Xiangyu Zhu, Honglei Yin, Yu Zheng…

TalkRL: The Reinforcement Learning Podcast

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Eugene Vinitsky 1:06:02

il y a 4 years1:06:02

1:06:02

Eugene Vinitsky is a PhD student at UC Berkeley advised by Alexandre Bayen. He has interned at Tesla and Deepmind. Featured References A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings Eugene Vinitsky, Raphael Köster, John P. Agapiou, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, Joel Z. Leibo Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL Eugene Vinitsky, Nathan Lichtle, Kanaad Parvate, Alexandre Bayen Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion Eugene Vinitsky; Kanaad Parvate; Aboudy Kreidieh; Cathy Wu; Alexandre Bayen 2018 The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu Additional References SUMO: Simulation of Urban MObility…

TalkRL: The Reinforcement Learning Podcast

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Jess Whittlestone 1:31:36

il y a 4 years1:31:36

1:31:36

Dr. Jess Whittlestone is a Senior Research Fellow at the Centre for the Study of Existential Risk and the Leverhulme Centre for the Future of Intelligence, both at the University of Cambridge. Featured References The Societal Implications of Deep Reinforcement Learning Jess Whittlestone, Kai Arulkumaran, Matthew Crosby Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI Carla Zoe Cremer, Jess Whittlestone Additional References CogX: Cutting Edge: Understanding AI systems for a better AI policy , featuring Jack Clark and Jess Whittlestone…

TalkRL: The Reinforcement Learning Podcast

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Aleksandra Faust 54:30

il y a 4 years54:30

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Dr Aleksandra Faust is a Staff Research Scientist and Reinforcement Learning research team co-founder at Google Brain Research. Featured References Reinforcement Learning and Planning for Preference Balancing Tasks Faust 2014 Learning Navigation Behaviors End-to-End with AutoRL Hao-Tien Lewis Chiang, Aleksandra Faust, Marek Fiser, Anthony Francis Evolving Rewards to Automate Reinforcement Learning Aleksandra Faust, Anthony Francis, Dar Mehta Evolving Reinforcement Learning Algorithms John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust Adversarial Environment Generation for Learning to Navigate the Web Izzeddin Gur, Natasha Jaques, Kevin Malta, Manoj Tiwari, Honglak Lee, Aleksandra Faust Additional References AutoML-Zero: Evolving Machine Learning Algorithms From Scratch , Esteban Real, Chen Liang, David R. So, Quoc V. Le…

TalkRL: The Reinforcement Learning Podcast

1
Sam Ritter 1:40:35

il y a 4 years1:40:35

1:40:35

Sam Ritter is a Research Scientist on the neuroscience team at DeepMind. Featured References Unsupervised Predictive Memory in a Goal-Directed Agent (MERLIN) Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matt Botvinick, Demis Hassabis, Timothy Lillicrap Meta-RL without forgetting: Been There, Done That: Meta-Learning with Episodic Recall Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick Meta-Reinforcement Learning with Episodic Recall: An Integrative Theory of Reward-Driven Learning Samuel Ritter 2019 Meta-RL exploration and planning: Rapid Task-Solving in Novel Environments Sam Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matt Botvinick, David Raposo Synthetic Returns for Long-Term Credit Assignment David Raposo, Sam Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt Botvinick, Hado van Hasselt, Francis Song Additional References Sam Ritter: Meta-Learning to Make Smart Inferences from Small Data , North Star AI 2019 The Bitter Lesson , Rich Sutton 2019…

TalkRL: The Reinforcement Learning Podcast

1
Thomas Krendl Gilbert 1:12:14

il y a 4 years1:12:14

1:12:14

Thomas Krendl Gilbert is a PhD student at UC Berkeley’s Center for Human-Compatible AI , specializing in Machine Ethics and Epistemology. Featured References Hard Choices in Artificial Intelligence: Addressing Normative Uncertainty through Sociotechnical Commitments Roel Dobbe, Thomas Krendl Gilbert, Yonatan Mintz Mapping the Political Economy of Reinforcement Learning Systems: The Case of Autonomous Vehicles Thomas Krendl Gilbert AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks McKane Andrus, Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert and Tom Zick Additional References Political Economy of Reinforcement Learning Systems (PERLS) The Law and Political Economy (LPE) Project The Societal Implications of Deep Reinforcement Learning , Jess Whittlestone, Kai Arulkumaran, Matthew Crosby Robot Brains Podcast: Yann LeCun explains why Facebook would crumble without AI…

TalkRL: The Reinforcement Learning Podcast

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Marc G. Bellemare 57:40

il y a 4 years57:40

57:40

Professor Marc G. Bellemare is a Research Scientist at Google Research (Brain team), An Adjunct Professor at McGill University, and a Canada CIFAR AI Chair. Featured References The Arcade Learning Environment: An Evaluation Platform for General Agents Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling Human-level control through deep reinforcement learning Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis Hassabis Autonomous navigation of stratospheric balloons using reinforcement learning Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra, Sameera S. Ponda & Ziyu Wang Additional References CAIDA Talk: A tour of distributional reinforcement learning November 18, 2020 - Marc G. Bellemare Amii AI Seminar Series: Autonomous nav of stratospheric balloons using RL , Marlos C. Machado UMD RLSS | Marc Bellemare | A History of Reinforcement Learning: Atari to Stratospheric Balloons TalkRL: Marlos C. Machado , Dr. Machado also spoke to us about various aspects of ALE and Project Loon in depth Hyperbolic discounting and learning over multiple horizons , Fedus et al 2019 Marc G. Bellemare on Twitter…

TalkRL: The Reinforcement Learning Podcast

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Robert Osazuwa Ness 1:18:43

il y a 4 years1:18:43