Improve the efficiency of deep reinforcement learning through semantic
exploration guided by natural language

Improve the efficiency of deep reinforcement learning through semantic exploration guided by natural language

21 September 2023

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Papers citing "Improve the efficiency of deep reinforcement learning through semantic exploration guided by natural language"

19 / 19 papers shown

Title
Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction Anthony GX-Chen Kenneth Marino Rob Fergus OCL 103 1 0 21 Aug 2024
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL Thomas Carta Pierre-Yves Oudeyer Olivier Sigaud Sylvain Lamprier OffRL 48 24 0 20 Jun 2022
Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language Iou-Jen Liu Xingdi Yuan Marc-Alexandre Côté Pierre-Yves Oudeyer Alex Schwing RALM 73 12 0 12 May 2022
Semantic Exploration from Language Abstractions and Pretrained Representations Allison C. Tam Neil C. Rabinowitz Andrew Kyle Lampinen Nicholas A. Roy Stephanie C. Y. Chan D. Strouse Jane X. Wang Andrea Banino Felix Hill LM&Ro 91 70 0 08 Apr 2022
Inferring Rewards from Language in Context Jessy Lin Daniel Fried Dan Klein Anca Dragan LM&Ro 81 55 0 05 Apr 2022
Dynamic population-based meta-learning for multi-agent communication with natural language Abhinav Gupta Marc Lanctot Angeliki Lazaridou LLMAG 144 20 0 27 Oct 2021
Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation Suraj Nair E. Mitchell Kevin Chen Brian Ichter Silvio Savarese Chelsea Finn LM&Ro OffRL 105 158 0 02 Sep 2021
ELLA: Exploration through Learned Language Abstraction Suvir Mirchandani Siddharth Karamcheti Dorsa Sadigh LLMAG 48 58 0 10 Mar 2021
Learning Rewards from Linguistic Feedback T. Sumers Mark K. Ho Robert D. Hawkins Karthik Narasimhan Thomas Griffiths 113 54 0 30 Sep 2020
Learning with AMIGo: Adversarially Motivated Intrinsic Goals Andres Campero Roberta Raileanu Heinrich Küttler J. Tenenbaum Tim Rocktaschel Edward Grefenstette 84 127 0 22 Jun 2020
Emergent Multi-Agent Communication in the Deep Learning Era Angeliki Lazaridou Marco Baroni AI4CE 126 203 0 03 Jun 2020
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning Angeliki Lazaridou Anna Potapenko O. Tieleman LLMAG 121 98 0 14 May 2020
RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments Roberta Raileanu Tim Rocktaschel 67 173 0 27 Feb 2020
Language as an Abstraction for Hierarchical Deep Reinforcement Learning Yiding Jiang S. Gu Kevin Patrick Murphy Chelsea Finn OffRL 55 225 0 18 Jun 2019
A Survey of Reinforcement Learning Informed by Natural Language Jelena Luketina Nantas Nardelli Gregory Farquhar Jakob N. Foerster Jacob Andreas Edward Grefenstette Shimon Whiteson Tim Rocktaschel LM&Ro KELM OffRL LRM 82 282 0 10 Jun 2019
Using Natural Language for Reward Shaping in Reinforcement Learning Prasoon Goyal S. Niekum Raymond J. Mooney LM&Ro 90 182 0 05 Mar 2019
Interactive Grounded Language Acquisition and Generalization in a 2D World Haonan Yu Haichao Zhang Wenyuan Xu LLMAG LM&Ro 119 79 0 31 Jan 2018
Deal or No Deal? End-to-End Learning for Negotiation Dialogues M. Lewis Denis Yarats Yann N. Dauphin Devi Parikh Dhruv Batra LLMAG 91 414 0 16 Jun 2017
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning Justin Johnson B. Hariharan Laurens van der Maaten Li Fei-Fei C. L. Zitnick Ross B. Girshick CoGe 304 2,378 0 20 Dec 2016

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