ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2210.09566
  4. Cited By
Simple Emergent Action Representations from Multi-Task Policy Training
v1v2 (latest)

Simple Emergent Action Representations from Multi-Task Policy Training

International Conference on Learning Representations (ICLR), 2022
18 October 2022
Pu Hua
Yubei Chen
Huazhe Xu
    MLAU
ArXiv (abs)PDFHTML

Papers citing "Simple Emergent Action Representations from Multi-Task Policy Training"

5 / 5 papers shown
Title
What Do Latent Action Models Actually Learn?
What Do Latent Action Models Actually Learn?International Conference on Learning Representations (ICLR), 2024
Chuheng Zhang
Tim Pearce
Pushi Zhang
Kaixin Wang
Xiaoyu Chen
Wei Shen
Li Zhao
Jiang Bian
137
7
0
27 May 2025
Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning
Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement LearningComputer Vision and Pattern Recognition (CVPR), 2025
Chenjie Hao
Weyl Lu
Yifan Xu
Yubei Chen
148
4
0
09 Apr 2025
DCT: Dual Channel Training of Action Embeddings for Reinforcement
  Learning with Large Discrete Action Spaces
DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action SpacesAdaptive Agents and Multi-Agent Systems (AAMAS), 2023
Pranavi Pathakota
Hardik Meisheri
H. Khadilkar
OffRL
124
1
0
28 Jun 2023
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual
  Reinforcement Learning
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement LearningNeural Information Processing Systems (NeurIPS), 2023
Ruijie Zheng
Xiyao Wang
Yanchao Sun
Shuang Ma
Jieyu Zhao
Huazhe Xu
Hal Daumé
Furong Huang
208
59
0
22 Jun 2023
Rewarded soups: towards Pareto-optimal alignment by interpolating
  weights fine-tuned on diverse rewards
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardsNeural Information Processing Systems (NeurIPS), 2023
Alexandre Ramé
Guillaume Couairon
Mustafa Shukor
Corentin Dancette
Jean-Baptiste Gaya
Laure Soulier
Matthieu Cord
MoMe
339
198
0
07 Jun 2023
1