TalkRL: The Reinforcement Learning podcast

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

28:30

We're trying something different this week: a full post-show breakdown of every episode in the latest season of Black Mirror! Ari Romero is joined by Tudum's Black Mirror expert, Keisha Hatchett, to give you all the nuance, the insider commentary, and the details you might have missed in this incredible new season. Plus commentary from creator & showrunner Charlie Brooker! SPOILER ALERT: We're talking about the new season in detail and revealing key plot points. If you haven't watched yet, and you don't want to know what happens, turn back now! You can watch all seven seasons of Black Mirror now in your personalized virtual theater . Follow Netflix Podcasts and read more about Black Mirror on Tudum.com .…

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

89 subscribers

updated about a year ago

Series home•Feed

66 episodes

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

Semua episode

1
NeurIPS 2024 - Posters and Hallways 3 10:01

5 weeks ago10:01

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…

1
NeurIPS 2024 - Posters and Hallways 2 8:48

6 weeks ago8:48

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…

1
NeurIPS 2024 - Posters and Hallways 1 9:32

6 weeks ago9:32

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

9 weeks ago1:21:40

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)…

1
Neurips 2024 RL meetup Hot takes: What sucks about RL? 17:45

16 weeks ago17:45

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

30 weeks ago13:17

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…

1
RLC 2024 - Posters and Hallways 4 4:52

30 weeks ago4:52

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…

1
RLC 2024 - Posters and Hallways 3 6:43

30 weeks ago6:43

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

30 weeks ago15:52

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

31 weeks ago5:46

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…

1
Finale Doshi-Velez on RL for Healthcare @ RCL 2024 7:35

32 weeks ago7:35

7:35

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

33 weeks ago16:17

16:17

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

33 weeks ago11:27

11:27

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

1 year ago40:14

40:14

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

1 year ago33:30