Papers
arxiv:1803.01118
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Published on Jan 11, 2019
Authors:
Abstract
Two meta reinforcement learning algorithms improve exploration performance on novel maze and Krazy World tasks.
Generated by thinkingmachines/Inkling-Small
We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-RL^2. Results are presented on a novel environment we call `Krazy World' and a set of maze environments. We show E-MAML and E-RL^2 deliver better performance on tasks where exploration is important.
Get this paper in your agent:
hf papers read 1803.01118 Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/1803.01118 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/1803.01118 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/1803.01118 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.