Cross-Domain Policy Optimization via Bellman Consistency and Hybrid Critics

Export BriefConnect with Author
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 (52)

[1]
Semi-Supervised Cross-Domain Imitation Learning
2026Li-Min Chu, Kai Ma et al.
[2]
Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs
2025Wei-Ting Hung, Shao-Hua Sun et al.
[3]
Cross-Domain Policy Adaptation by Capturing Representation Mismatch
2024Jiafei Lyu, Chenjia Bai et al.
[4]
Cross Domain Policy Transfer with Effect Cycle-Consistency
2024Ruiqi Zhu, Tianhong Dai et al.
[5]
Recurrent Reinforcement Learning with Memoroids
2024Steven D. Morad, Chris Lu et al.
[6]
FlowPG: Action-constrained Policy Gradient with Normalizing Flows
2024J. Brahmanage, Jiajing Ling et al.
[7]
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
2023Yifei Zhou, Ayush Sekhari et al.
[8]
Domain-Aware Fine-Tuning: Enhancing Neural Network Adaptability
2023Seokhyeon Ha, S. Jung et al.
[9]
Cross-domain policy adaptation with dynamics alignment
2023Haiyuan Gui, Shanchen Pang et al.
[10]
Cross-Domain Policy Adaptation via Value-Guided Data Filtering
2023Kang Xu, Chenjia Bai et al.
[11]
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
2022Yuda Song, Yi Zhou et al.
[12]
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies
2022Rui Yuan, S. Du et al.
[13]
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning
2022Jinxin Liu, Hongyin Zhang et al.
[14]
Cross-domain adaptive transfer reinforcement learning based on state-action correspondence
2022Heng You, Tianpei Yang et al.
[15]
Cross-Domain Imitation Learning via Optimal Transport
2021Arnaud Fickinger, Samuel Cohen et al.
[16]
Policy invariant explicit shaping: an efficient alternative to reward shaping
2021Paniz Behboudian, Yash Satsangi et al.
[17]
Deep Reinforcement Learning at the Edge of the Statistical Precipice
2021Rishabh Agarwal, Max Schwarzer et al.
[18]
Auto-Tuned Sim-to-Real Transfer
2021Yuqing Du, Olivia Watkins et al.
[19]
Stable-Baselines3: Reliable Reinforcement Learning Implementations
2021A. Raffin, Ashley Hill et al.
[20]
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency
2020Qiang Zhang, Tete Xiao et al.

Showing 20 of 52 references

Founder's Pitch

"QAvatar enhances cross-domain reinforcement learning by effectively leveraging source-domain knowledge for improved transferability."

Reinforcement LearningScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

2/4 signals

5

Series A Potential

3/4 signals

7.5

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: 3/12/2026

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.

Related Papers

Loading…

Related Resources