Risto Vuorio's guest appearances

Risto Vuorio

TalkRL: The Reinforcement Learning Podcast

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Jacob Beck

Jacob Beck and Risto Vuorio on their recent Survey of Meta-Reinforcement Learning.  Jacob and Risto are Ph.D. students at Whiteson Research Lab at University of Oxford.    Featured Reference   A Survey of Meta-Reinforcement LearningJacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, Shimon Whiteson   Additional References    VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning, Luisa Zintgraf et al    Mastering Diverse Domains through World Models (Dreamerv3), Hafner et al      Unsupervised Meta-Learning for Reinforcement Learning (MAML), Gupta et al    Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices (DREAM), Liu et al    RL2: Fast Reinforcement Learning via Slow Reinforcement Learning, Duan et al    Learning to reinforcement learn, Wang et al   

Jacob Beck and Risto Vuorio on their recent Survey of Meta-Reinforcement Learning.  Jacob and Risto are Ph.D. students at Whiteson Research Lab at University of Oxford.    Featured Reference    <a href="https://arxiv.org/abs/2301.08028" rel="nofollow" target="_blank">A Survey of Meta-Reinforcement Learning </a>Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, Shimon Whiteson    Additional References   <ul> <li> <a href="https://arxiv.org/abs/1910.08348" rel="nofollow" target="_blank">VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning</a>, Luisa Zintgraf et al  </li> <li> <a href="https://arxiv.org/abs/2301.04104v1" rel="nofollow" target="_blank">Mastering Diverse Domains through World Models</a> (Dreamerv3), Hafner et al    </li> <li> <a href="https://arxiv.org/abs/1806.04640" rel="nofollow" target="_blank">Unsupervised Meta-Learning for Reinforcement Learning</a> (MAML), Gupta et al  </li> <li> <a href="https://arxiv.org/abs/2008.02790" rel="nofollow" target="_blank">Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices</a> (DREAM), Liu et al  </li> <li> <a href="https://arxiv.org/abs/1611.02779" rel="nofollow" target="_blank">RL2: Fast Reinforcement Learning via Slow Reinforcement Learning</a>, Duan et al  </li> <li> <a href="https://arxiv.org/abs/1611.05763" rel="nofollow" target="_blank">Learning to reinforcement learn</a>, Wang et al  </li> </ul>