1 code implementation • 16 Mar 2024 • Sai Prasanna, Karim Farid, Raghu Rajan, André Biedenkapp
Toward the goal of ZSG to unseen variation in context, we propose the contextual recurrent state-space model (cRSSM), which introduces changes to the world model of the Dreamer (v3) (Hafner et al., 2023).
1 code implementation • 19 Sep 2022 • Iman Nematollahi, Erick Rosete-Beas, Seyed Mahdi B. Azad, Raghu Rajan, Frank Hutter, Wolfram Burgard
To the best of our knowledge, our model is the first generative model that provides an RGB-D video prediction of the future for a static camera.
no code implementations • 11 Jan 2022 • Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandra Faust, Frank Hutter, Marius Lindauer
The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents.
1 code implementation • 9 Jun 2021 • André Biedenkapp, Raghu Rajan, Frank Hutter, Marius Lindauer
Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment.
1 code implementation • 26 Feb 2021 • Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra
We demonstrate that this problem can be tackled effectively with automated HPO, which we demonstrate to yield significantly improved performance compared to human experts.
Hyperparameter Optimization Model-based Reinforcement Learning +2
no code implementations • 28 Sep 2020 • Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp, Frank Hutter
We present MDP Playground, an efficient benchmark for Reinforcement Learning (RL) algorithms with various dimensions of hardness that can be controlled independently to challenge algorithms in different ways and to obtain varying degrees of hardness in generated environments.
1 code implementation • 17 Sep 2019 • Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp, Jan Ole von Hartz, Frank Hutter
We define a parameterised collection of fast-to-run toy environments in OpenAI Gym by varying these dimensions and propose to use these to understand agents better.