View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (30)

[1]
Retrieval Dexterity: Efficient Object Retrieval in Clutters with Dexterous Hand
2025Fengshuo Bai, Yu Li et al.
[2]
AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning
2025Yuanfei Wang, Xiaojie Zhang et al.
[3]
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors
2024Fengshuo Bai, Runze Liu et al.
[4]
Trust the Model Where It Trusts Itself - Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption
2024Bernd Frauenknecht, Artur Eisele et al.
[5]
Reinformer: Max-Return Sequence Modeling for Offline RL
2024Zifeng Zhuang, Dengyun Peng et al.
[6]
Fast Peer Adaptation with Context-aware Exploration
2024Long Ma, Yuanfei Wang et al.
[7]
TD-MPC2: Scalable, Robust World Models for Continuous Control
2023Nicklas Hansen, Hao Su et al.
[8]
PiCor: Multi-Task Deep Reinforcement Learning with Policy Correction
2023Fengshuo Bai, Hongming Zhang et al.
[9]
MoCoDA: Model-based Counterfactual Data Augmentation
2022Silviu Pitis, Elliot Creager et al.
[10]
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
2021Yuan-Fang Wang, Fangwei Zhong et al.
[11]
Offline Reinforcement Learning with Implicit Q-Learning
2021Ilya Kostrikov, Ashvin Nair et al.
[12]
Offline Reinforcement Learning with Reverse Model-based Imagination
2021Jianhao Wang, Wenzhe Li et al.
[13]
A Minimalist Approach to Offline Reinforcement Learning
2021Scott Fujimoto, S. Gu
[14]
A graph placement methodology for fast chip design
2021Azalia Mirhoseini, Anna Goldie et al.
[15]
How to train your robot with deep reinforcement learning: lessons we have learned
2021Julian Ibarz, Jie Tan et al.
[16]
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
2020Alexey Dosovitskiy, Lucas Beyer et al.
[17]
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
2020S. Levine, Aviral Kumar et al.
[18]
Reinforcement Learning with Augmented Data
2020M. Laskin, Kimin Lee et al.
[19]
D4RL: Datasets for Deep Data-Driven Reinforcement Learning
2020Justin Fu, Aviral Kumar et al.
[20]
Deep Reinforcement Learning for Autonomous Driving: A Survey
2020B. R. Kiran, Ibrahim Sobh et al.

Showing 20 of 30 references

Founder's Pitch

"A new framework increases offline RL performance by enhancing dataset quality with Imaginary Planning Distillation."

Reinforcement LearningScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 3/4/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.