Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies | Paper Citation Record

Observation f31cc6b1-dd95-41d7-a058-ac0fba07e79b · outbound

This paper cites Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

Reference 1

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Observation 92cfd7de-2a3c-40c0-a7fb-d1657f3d4f4a · outbound

This paper cites DeepSeek-V3 Technical Report.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies DeepSeek-V3 Technical Report

Reference 8

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Observation 296bd663-b3a3-44d7-bc7d-4bd91fe21d45 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 9

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This paper cites Girshick.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Girshick

Reference 11

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Observation 96e5cc89-4cfe-4d15-ab9a-c4abf82b6a4d · outbound

This paper cites Scaling Laws for Autoregressive Generative Modeling.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Scaling Laws for Autoregressive Generative Modeling

Reference 12

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This paper cites Scaling Laws for Neural Language Models.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Scaling Laws for Neural Language Models

Reference 15

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Observation 47ec75fa-639c-468d-930a-43b7db5d5488 · outbound

This paper cites Kimi K2: Open Agentic Intelligence.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Kimi K2: Open Agentic Intelligence

Reference 16

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Observation 6bf65cda-86bb-4f74-8cc7-f8bb7f90a255 · outbound

This paper cites SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

Reference 18

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Observation c276368b-ee5a-4e92-a097-6a6b93eaab6a · outbound

This paper cites Hyperspherical Normalization for Scalable Deep Reinforcement Learning.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Hyperspherical Normalization for Scalable Deep Reinforcement Learning

Reference 19

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Observation 8aeb3b80-7dba-4d80-92f9-104d6f66f7a0 · outbound

This paper cites HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach

Reference 21

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Observation 56a88053-8484-4f20-8e2e-d2afacf86fab · outbound

This paper cites Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model

Reference 22

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Observation 07612d32-42f0-4bb3-aa7b-7ae7f7c8a48a · outbound

This paper cites Encouraging divergent thinking in large language models through multi-agent debate.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Encouraging divergent thinking in large language models through multi-agent debate

Reference 23

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Observation ae716863-0aee-4800-8dcb-9cc490fe3269 · outbound

This paper cites Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers

Reference 24

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Observation e62efe48-f42d-494a-8f2f-e426e9358a9c · outbound

This paper cites Continuous control with deep reinforcement learning.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Continuous control with deep reinforcement learning

Reference 25

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Observation 74b92fd2-80fe-457e-8dfc-f7d363cc0b3b · outbound

This paper cites Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training

Reference 26

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Observation fc48908a-06b0-4ce2-b747-1ddc66b74c69 · outbound

This paper cites Evolutionary Action Selection for Gradient-based Policy Learning.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Evolutionary Action Selection for Gradient-based Policy Learning

Reference 27

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Observation 51f80b10-ba54-4325-9ee9-7544de71c491 · outbound

This paper cites Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

Reference 28

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Observation 92c82929-f926-4f15-9a69-7230b4fb38e3 · outbound

This paper cites SmolVLM: Redefining small and efficient multimodal models.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies SmolVLM: Redefining small and efficient multimodal models

Reference 29

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Observation b41a1983-3b77-479e-b2b4-547f72c1f91f · outbound

This paper cites The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks

Reference 30

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Observation da9c01ce-fac7-4106-a460-3eab38e783ba · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Playing Atari with Deep Reinforcement Learning

Reference 31

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Observation 4ac0e78e-37c2-4042-a3bb-e7e062983975 · outbound

This paper cites s1: Simple test-time scaling.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies s1: Simple test-time scaling

Reference 33

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This paper cites Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners

Reference 34

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Observation d69bd5f6-b29c-4465-8e1a-1022b874b711 · outbound

This paper cites Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size

Reference 35

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This paper cites GPT-4 Technical Report.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies GPT-4 Technical Report

Reference 36

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This paper cites Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization

Reference 37

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Generative Agents: Interactive Simulacra of Human Behavior

Reference 38

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Horizon reduction makes rl scalable

Reference 39

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This paper cites Qwen3 Technical Report.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Qwen3 Technical Report

Reference 40

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Value-Based Deep RL Scales Predictably

Reference 41

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Proximal Policy Optimization Algorithms

Reference 42

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This paper cites FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control

Reference 43

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation

Reference 44

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 45

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Q-Learning for Continuous Actions with Cross-Entropy Guided Policies

Reference 46

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Accelerated Methods for Deep Reinforcement Learning

Reference 47

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies DeepMind Control Suite

Reference 48

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This paper cites MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL

Reference 50

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This paper cites 1000 layer networks for self-supervised rl: Scaling depth can enable new goal-reaching capabilities.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies 1000 layer networks for self-supervised rl: Scaling depth can enable new goal-reaching capabilities

Reference 51

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Prioritized Generative Replay

Reference 52

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance

Reference 53

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning

Reference 54

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This paper cites ISBN 979-8-3503-5067-8.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies ISBN 979-8-3503-5067-8

Reference 55

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble

Reference 56

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning

Reference 57

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This paper cites doi: 10.3233/FAIA230609.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies doi: 10.3233/FAIA230609

Reference 58

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes

Reference 59

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies URLB: Unsupervised Reinforcement Learning Benchmark

Reference 1989

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Representation Learning with Contrastive Predictive Coding

Reference 2012

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This paper cites Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu

Reference 2015

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This paper cites MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control

Reference 2016

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Scaling laws for single-agent reinforcement learning

Reference 2017

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Simplifying Deep Temporal Difference Learning

Reference 2018

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies UCB Exploration via Q-Ensembles

Reference 2019

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This paper cites OpenAI Gym.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies OpenAI Gym

Reference 2020

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Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Phasic policy gradient

Reference 2021

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Observation b7addc73-5c8a-44e7-ac44-dd8f8b35bfbe · outbound

This paper cites Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents

Reference 2022

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Observation 4cb6c6bc-60cc-4f75-89d2-424822a76ed4 · outbound

This paper cites Dota 2 with Large Scale Deep Reinforcement Learning.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Dota 2 with Large Scale Deep Reinforcement Learning

Reference 2023

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This paper cites Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control

Reference 2024

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Observation 469c9732-ca93-42e9-ba3f-7db283b79c39 · outbound

This paper cites Gaon An, Seungyong Moon, Jang-Hyun Kim, and Hyun Oh Song.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Gaon An, Seungyong Moon, Jang-Hyun Kim, and Hyun Oh Song

Reference 2025

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