All Episodes

Displaying episodes 1 - 30 of 38 in total

Sven Mika

Sven Mika of Anyscale on RLlib present and future, Ray and Ray Summit 2022, applied RL in Games / Finance / RecSys, and more!

Karol Hausman and Fei Xia

Karol Hausman and Fei Xia of Google Research on newly updated (PaLM-)SayCan, Inner Monologue, robot learning, combining robotics with language models, and more!

Sai Krishna Gottipati

Sai Krishna Gottipati of AI Redefined on RL for synthesizable drug discovery, Multi-Teacher Self-Play, Cogment framework for realtime multi-actor RL, AI + Chess, and m...

Aravind Srinivas 2

Aravind Srinivas, Research Scientist at OpenAI, returns to talk Decision Transformer, VideoGPT, choosing problems, and explore vs exploit in research careers

Rohin Shah

DeepMind Research Scientist Dr. Rohin Shah on Value Alignment, Learning from Human feedback, Assistance paradigm, the BASALT MineRL competition, his Alignment Newslett...

Jordan Terry

Jordan Terry on maintaining Gym and PettingZoo, hardware accelerated environments and the future of RL, environment models for multi-agent RL, and more!

Robert Lange

Robert Lange on learning vs hard-coding, meta-RL, Lottery Tickets and Minimal Task Representations, Action Grammars and more!

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

Dr. Thomas Gilbert and Dr. Mark Nitzberg on the upcoming PERLS Workshop @ NeurIPS 2021

Amy Zhang

Amy Zhang shares her work on Invariant Causal Prediction for Block MDPs, Multi-Task Reinforcement Learning with Context-based Representations, MBRL-Lib, shares insight...

Xianyuan Zhan

Xianyuan Zhan on DeepThermal for controlling thermal power plants, the MORE algorithm for Model-based Offline RL, comparing AI in China and the US, and more!

Eugene Vinitsky

Eugene Vinitsky of UC Berkeley on social norms and sanctions, traffic simulation, mixed-autonomy traffic, and more!

Jess Whittlestone

Jess Whittlestone on societal implications of deep reinforcement Learning, AI policy, warning signs of transformative progress in AI, and more!

Aleksandra Faust

Aleksandra Faust of Google Brain Research on AutoRL, meta-RL, learning to learn & learning to teach, curriculum learning, collaborations between senior and junior ...

Sam Ritter

Sam Ritter of DeepMind on Neuroscience and RL, Episodic Memory, Meta-RL, Synthetic Returns, the MERLIN agent, decoding brain activation, and more!

Thomas Krendl Gilbert

Thomas Krendl Gilbert on the Political Economy of Reinforcement Learning Systems & Autonomous Vehicles, Sociotechnical Commitments, AI Development for the Public I...

Marc G. Bellemare

Marc G. Bellemare shares insight on his work including Deep Q-Networks, Distributional RL, Project Loon and RL in the Stratosphere, the origins of the Arcade Learning ...

Robert Osazuwa Ness

Dr. Robert Osazuwa Ness on Causal Inference, Probabilistic and Generative Models, Causality and RL, AltDeep School of AI, Pyro, and more!

Marlos C. Machado

Marlos C. Machado on Arcade Learning Environment Evaluation, Generalization and Exploration in RL, Eigenoptions, Autonomous navigation of stratospheric balloons with R...

Nathan Lambert

Nathan Lambert on Model-based RL, Trajectory-based models, Quadrotor control, Hyperparameter Optimization for MBRL, RL vs PID control, and more!

Kai Arulkumaran

Kai Arulkumaran on AlphaStar and Evolutionary Computation, Domain Randomisation, Upside-Down Reinforcement Learning, Araya, NNAISENSE, and more!

Michael Dennis

Michael Dennis on Human-Compatible AI, Game Theory, PAIRED, ARCTIC, EPIC, and lots more!

Roman Ring

Roman Ring discusses the Research Engineer role at DeepMind, StarCraft II, AlphaStar, his bachelor's thesis, JAX, Julia, IMPALA and more!

Shimon Whiteson

Shimon Whiteson on his WhiRL lab, his work at Waymo UK, variBAD, QMIX, co-operative multi-agent RL, StarCraft Multi-Agent Challenge, advice to grad students, and much ...

Aravind Srinivas

Aravind Srinivas on his work including CPC v2, RAD, CURL, and SUNRISE, unsupervised learning, teaching a Berkeley course, and more!

Taylor Killian

Taylor Killian on the latest in RL for Health, including Hidden Parameter MDPs, Mimic III and Sepsis, Counterfactually Guided Policy Transfer and lots more!

Nan Jiang

Nan Jiang takes us deep into Model-based vs Model-free RL, Sim vs Real, Evaluation & Overfitting, RL Theory vs Practice and much more!

Danijar Hafner

Danijar Hafner takes us on an odyssey through deep learning & neuroscience, PlaNet, Dreamer, world models, latent dynamics, curious agents, and more!

Csaba Szepesvari

Csaba Szepesvari of DeepMind shares his views on Bandits, Adversaries, PUCT in AlphaGo / AlphaZero / MuZero, AGI and RL, what is timeless, and more!

Ben Eysenbach

Ben Eysenbach schools us on human supervision, SORB, DIAYN, techniques for exploration, teaching RL, virtual conferences, and much more!

NeurIPS 2019 Deep RL Workshop

Hear directly from presenters at the NeurIPS 2019 Deep RL Workshop on their work!

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