Displaying episodes 1 - 30 of 32 in total
Robert Lange on learning vs hard-coding, meta-RL, Lottery Tickets and Minimal Task Representations, Action Grammars and more!
Dr. Thomas Gilbert and Dr. Mark Nitzberg on the upcoming PERLS Workshop @ NeurIPS 2021
Amy Zhang shares her work on Invariant Causal Prediction for Block MDPs, Multi-Task Reinforcement Learning with Context-based Representations, MBRL-Lib, shares insight on generalization on RL, and more!
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 of UC Berkeley on social norms and sanctions, traffic simulation, mixed-autonomy traffic, and more!
Jess Whittlestone on societal implications of deep reinforcement Learning, AI policy, warning signs of transformative progress in AI, and more!
Aleksandra Faust of Google Brain Research on AutoRL, meta-RL, learning to learn & learning to teach, curriculum learning, collaborations between senior and junior researchers, and more!
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 on the Political Economy of Reinforcement Learning Systems & Autonomous Vehicles, Sociotechnical Commitments, AI Development for the Public Interest, and more!
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 Environment, the future of Benchmarking in RL -- and more!
Dr. Robert Osazuwa Ness on Causal Inference, Probabilistic and Generative Models, Causality and RL, AltDeep School of AI, Pyro, and more!
Marlos C. Machado on Arcade Learning Environment Evaluation, Generalization and Exploration in RL, Eigenoptions, Autonomous navigation of stratospheric balloons with RL, and more!
Nathan Lambert on Model-based RL, Trajectory-based models, Quadrotor control, Hyperparameter Optimization for MBRL, RL vs PID control, and more!
Kai Arulkumaran on AlphaStar and Evolutionary Computation, Domain Randomisation, Upside-Down Reinforcement Learning, Araya, NNAISENSE, and more!
Michael Dennis on Human-Compatible AI, Game Theory, PAIRED, ARCTIC, EPIC, and lots more!
Roman Ring discusses the Research Engineer role at DeepMind, StarCraft II, AlphaStar, his bachelor's thesis, JAX, Julia, IMPALA and more!
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 more!
Aravind Srinivas on his work including CPC v2, RAD, CURL, and SUNRISE, unsupervised learning, teaching a Berkeley course, and more!
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 takes us deep into Model-based vs Model-free RL, Sim vs Real, Evaluation & Overfitting, RL Theory vs Practice and much more!
Danijar Hafner takes us on an odyssey through deep learning & neuroscience, PlaNet, Dreamer, world models, latent dynamics, curious agents, and more!
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 schools us on human supervision, SORB, DIAYN, techniques for exploration, teaching RL, virtual conferences, and much more!
Hear directly from presenters at the NeurIPS 2019 Deep RL Workshop on their work!
Scott Fujimoto expounds on his TD3 and BCQ algorithms, DDPG, Benchmarking Batch RL, and more!
Jessica Hamrick sheds light on Model-based RL, Structured agents, Mental simulation, Metacontrol, Construction environments, Blueberries, and more!
Pablo Samuel Castro drops in and drops knowledge on distributional RL, bisimulation, the Dopamine RL Framework, TF-Agents, and much more!
Kamyar Azizzadenesheli brings us insight on Bayesian RL, Generative Adversarial Tree search, what goes into great RL papers, and much more!
Antonin Raffin and Ashley Hill discuss Stable Baselines past, present and future, State Representation Learning, S-RL Toolbox, RL on real robots, big compute for RL and much more!
ACM Fellow Professor Michael L Littman enlightens us on Human feedback in RL, his Udacity courses, Theory of Mind, organizing the RLDM Conference, RL past and present, Hollywood cameos, and much more!
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© 2021 Robin Ranjit Singh Chauhan