Research map / Reinforcement learning / Visual and representation RL
Hierarchical Successor Representation for Robust Transfer
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
- Better Transfer Learning with Inferred Successor Maps (NeurIPS 2019 · similarity 0.89)
- Reward-Aware Proto-Representations in Reinforcement Learning (NeurIPS 2025 · similarity 0.89)
- PcLast: Discovering Plannable Continuous Latent States (ICML 2024 · similarity 0.88)
- Learning Successor Features the Simple Way (NeurIPS 2024 · similarity 0.86)
- Reconciling Spatial and Temporal Abstractions for Goal Representation (ICLR 2024 · similarity 0.86)
- Spectral Alignment in Forward–Backward Representations via Temporal Abstraction (NeurIPS 2026 · 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.