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Some Considerations on Learning to Explore via Meta-Reinforcement Learning

2018-03-03 · ICLR 2018 1 · Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever

We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-$\text{RL}^2$. Results are presented on a novel environment we call `Krazy World' and a set of maze environments. We show E-MAML and E-$\text{RL}^2$ deliver better performance on tasks where exploration is important.

📄 PDF Abstract BibTeX arXiv:1803.01118

Code (7)

episodeyang/e-maml 공식 구현 tf

Zhiwei-Z/PrompLimitTest tf

Zhiwei-Z/SeqPromp tf

Zhiwei-Z/prompzzw tf

clrrrr/promp_plus tf

jonasrothfuss/promp tf

mazpie/mime pytorch

Tasks

Meta Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Meta-learning Extractors for Music Source Separation

2020-02-17 · David Samuel, Aditya Ganeshan, Jason Naradowsky

We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models. This enables efficient parameter-…

Meta-Learning Music Source Separation

Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?

2019-02-26 · Céline Hocquette, Stephen H. Muggleton

World-class human players have been outperformed in a number of complex two person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems. However, owing to tractability considerations minimax regret of a lea…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic

2023-02-28 · Syu-Ning Johnn, Victor-Alexandru Darvariu, Julia Handl, Joerg Kalcsics

ALNS is a popular metaheuristic with renowned efficiency in solving combinatorial optimisation problems. However, despite 16 years of intensive research into ALNS, whether the embedded adaptive layer can efficiently sele…

Deep Reinforcement Learning Open-Ended Question Answering reinforcement-learning Reinforcement Learning+1

Generative methods for sampling transition paths in molecular dynamics

2022-05-05 · Tony Lelièvre, Geneviève Robin, Inass Sekkat, Gabriel Stoltz 외

Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking …

reinforcement-learning Reinforcement Learning (RL)

Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning

2017-11-04 · Richard Liaw, Sanjay Krishnan, Animesh Garg, Daniel Crankshaw 외

Rather than learning new control policies for each new task, it is possible, when tasks share some structure, to compose a "meta-policy" from previously learned policies. This paper reports results from experiments using…

Autonomous Driving Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+1