-
Machine Learning for Robotic Manipulation
The past decade has witnessed the tremendous successes of machine learni...
01/04/2021 ∙ by Quan Vuong, et al. ∙
0
∙
share
read it
-
First Order Optimization in Policy Space for Constrained Deep Reinforcement Learning
In reinforcement learning, an agent attempts to learn high-performing be...
02/16/2020 ∙ by Yiming Zhang, et al. ∙
0
∙
share
read it
-
Better Exploration with Optimistic Actor-Critic
Actor-critic methods, a type of model-free Reinforcement Learning, have ...
10/28/2019 ∙ by Kamil Ciosek, et al. ∙
0
∙
share
read it
-
Towards Simplicity in Deep Reinforcement Learning: Streamlined Off-Policy Learning
The field of Deep Reinforcement Learning (DRL) has recently seen a surge...
10/05/2019 ∙ by Che Wang, et al. ∙
0
∙
share
read it
-
Pre-training as Batch Meta Reinforcement Learning with tiMe
Pre-training is transformative in supervised learning: a large network t...
09/25/2019 ∙ by Quan Vuong, et al. ∙
0
∙
share
read it
-
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
Recently, reinforcement learning (RL) algorithms have demonstrated remar...
03/28/2019 ∙ by Quan Vuong, et al. ∙
12
∙
share
read it
