Research map / Reinforcement learning / MDPs and planning
Partially Equivariant Reinforcement Learning in Symmetry-Breaking Environments
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
- ${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning (ICML 2024 · similarity 0.90)
- Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning (AAAI 2024 · similarity 0.90)
- Expected Return Symmetries (ICLR 2025 · similarity 0.89)
- Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance (NeurIPS 2024 · similarity 0.89)
- Latent Mixture of Symmetries for Sample-Efficient Dynamic Learning (NeurIPS 2025 · similarity 0.88)
- Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning (NeurIPS 2025 · similarity 0.88)
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.
