Deep reinforcement learning for robotics: A survey of real-world successes
Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
[LIBRO][B] Synthetic data for deep learning
SI Nikolenko - 2021 - Springer
You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
Parallel learning: Overview and perspective for computational learning across Syn2Real and Sim2Real
Q Miao, Y Lv, M Huang, X Wang… - IEEE/CAA Journal of …, 2023 - ieeexplore.ieee.org
The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …
solve real-world problems, has attracted more and more attention from various domains by …
Sim-to-real via sim-to-sim: Data-efficient robotic gras** via randomized-to-canonical adaptation networks
Real world data, especially in the domain of robotics, is notoriously costly to collect. One way
to circumvent this can be to leverage the power of simulation to produce large amounts of …
to circumvent this can be to leverage the power of simulation to produce large amounts of …
Identifying the risks of lm agents with an lm-emulated sandbox
Recent advances in Language Model (LM) agents and tool use, exemplified by applications
like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks-such …
like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks-such …