Mar 7, 2019 · Our methods can be naturally aligned with sliding trust region techniques for efficient samples reuse to further reduce sampling complexity.

Bibliographic details on When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies.

When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies. Resource URI: https://dblp.l3s.de/d2r/resource ...

Mar 7, 2019 · In this paper, we propose a new class of algorithms, called Robust Blackbox Optimization (RBO). Remarkably, even if up to 23\% of all the measurements are ...

When random search is not enough: Sample-efficient and noise-robust blackbox optimization of RL policies. K Choromanski, A Pacchiano, J Parker-Holder, J Hsu ...

When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies. Preprint. Full-text available. Mar 2019.

UFO-BLO: Unbiased First-Order Bilevel Optimization ... When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies ...

When random search is not enough: Sample-efficient and noise-robust blackbox optimization of RL policies. K Choromanski, A Pacchiano, J Parker-Holder, J Hsu ...

Jan 15, 2024 · In my personal experience, SOTA RL algorithms simply don't work. I've tried working with reinforcement learning for over 5 years.

When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies · jasmine Hsu. 2019, ArXiv ; Policy Gradient Bayesian ...