Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning | Paper Citation Record

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This paper cites write newline.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning write newline

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Devon Hjelm

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This paper cites Agent57: Outperforming the atari human benchmark.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Agent57: Outperforming the atari human benchmark

Reference 3

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This paper cites Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Dota 2 with Large Scale Deep Reinforcement Learning

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Experiment tracking with weights and biases, 2020

Reference 6

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This paper cites Selective particle attention: Rapidly and flexibly selecting features for deep reinforcement learning.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Selective particle attention: Rapidly and flexibly selecting features for deep reinforcement learning

Reference 7

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This paper cites RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Reference 8

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Generalized attention-weighted reinforcement learning

Reference 9

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Passaro, Vincenzo Lomonaco, Tinne Tuytelaars, and Davide Bacciu

Reference 10

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This paper cites HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning

Reference 11

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 12

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Unresolved cited work

Reference 13

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This paper cites Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training

Reference 14

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This paper cites Deep reservoir computing: A critical experimental analysis.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Deep reservoir computing: A critical experimental analysis

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Multimodal Masked Autoencoders Learn Transferable Representations

Reference 16

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Unsupervised video object segmentation for deep reinforcement learning

Reference 17

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This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, et al.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, et al

Reference 18

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Unresolved cited work

Reference 19

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This paper cites Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning

Reference 20

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This paper cites Unsupervised learning of object landmarks through conditional image generation.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Unsupervised learning of object landmarks through conditional image generation

Reference 21

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Continual pre-training of language models

Reference 22

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Openvla: An open-source vision-language-action model

Reference 23

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, et al

Reference 24

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Offline q-learning on diverse multi-task data both scales and generalizes

Reference 25

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Bootstrapped representations in reinforcement learning

Reference 26

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Instruction-Following Agents with Multimodal Transformer

Reference 27

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Swin transformer: Hierarchical vision transformer using shifted windows

Reference 28

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Unresolved cited work

Reference 29

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 30

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Rusu, Joel Veness, et al

Reference 31

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Exploiting semantic segmentation to boost reinforcement learning in video game environments

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning R3M: A Universal Visual Representation for Robot Manipulation

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Mixtures of experts unlock parameter scaling for deep RL

Reference 34

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Solving Rubik's Cube with a Robot Hand

Reference 35

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning DINOv2: Learning Robust Visual Features without Supervision

Reference 36

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0

Reference 37

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Learning Transferable Visual Models From Natural Language Supervision

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Rl baselines3 zoo

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Stable-baselines3: Reliable reinforcement learning implementations

Reference 40

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning The surprising ineffectiveness of pre-trained visual representations for model-based reinforcement learning

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 42

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Pretraining representations for data-efficient reinforcement learning

Reference 43

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Shah and Vikash Kumar

Reference 44

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Maddison, Arthur Guez, Laurent Sifre, et al

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Decoupling representation learning from reinforcement learning

Reference 46

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning PufferLib: Making Reinforcement Learning Libraries and Environments Play Nice

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Pufferlib 2.0: Reinforcement learning at 1m steps/s

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI

Reference 49

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Scaling Instructable Agents Across Many Simulated Worlds

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Building machines that learn and think like people

Reference 51

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Llama 2: Open Foundation and Fine-Tuned Chat Models

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Terry, Ariel Kwiatkowski, John U

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Attention is all you need

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This paper cites Czarnecki, Micha \" e l Mathieu, Andrew Dudzik, et al.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Czarnecki, Micha \" e l Mathieu, Andrew Dudzik, et al

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This paper cites A comprehensive survey of continual learning: Theory, method and application.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning A comprehensive survey of continual learning: Theory, method and application

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This paper cites SAPIEN : A simulated part-based interactive environment.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning SAPIEN : A simulated part-based interactive environment

Reference 57

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Masked Visual Pre-training for Motor Control

Reference 58

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This paper cites Pre-trained image encoder for generalizable visual reinforcement learning.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Pre-trained image encoder for generalizable visual reinforcement learning

Reference 59

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This paper cites Sigmoid loss for language image pre-training.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Sigmoid loss for language image pre-training

Reference 60

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This paper cites @esa (Ref.

Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning @esa (Ref

Reference 61

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Unresolved cited work

Reference 62

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Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning Unresolved cited work

Reference 63

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