Research map / Reinforcement learning / Offline RL
DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting (ICML 2024 · similarity 0.89)
- Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation (ICML 2025 · similarity 0.88)
- Improving Generalization in Offline Reinforcement Learning via Latent Distribution Representation Learning (AAAI 2025 · similarity 0.87)
- Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning (NeurIPS 2024 · similarity 0.87)
- Neural Network Approximation for Pessimistic Offline Reinforcement Learning (AAAI 2024 · similarity 0.87)
- Scaling Offline RL via Efficient and Expressive Shortcut Models (NeurIPS 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.