Research map / Reinforcement learning / Visual and representation RL
Successor features for transfer
Use successor representations and features to reuse value information across tasks and policies.
12 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2017–2026) · an approach in Visual and representation RL, keyword cluster “representation · latent · generalization”
State History Tried Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
3 papers in 2023–24 and 2 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 0.5% to 0.3%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2017 · Successor Features for Transfer in Reinforcement Learning (NeurIPS 2017 · 183 citations)
- 2019 · A neurally plausible model learns successor representations in partially observable environments (NeurIPS 2019 · 26 citations)
- 2021 · Risk-Aware Transfer in Reinforcement Learning using Successor Features (NeurIPS 2021 · 8 citations)
- 2022 · Constrained GPI for Zero-Shot Transfer in Reinforcement Learning (NeurIPS 2022 · 0 citations)
- 2023 · Combining Behaviors with the Successor Features Keyboard (NeurIPS 2023 · 1 citation)
- 2024 · Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making (ICML 2024 · 0 citations)
- 2025 · Reward-Aware Proto-Representations in Reinforcement Learning (NeurIPS 2025 · 0 citations)
- 2026 · Hierarchical Successor Representation for Robust Transfer (ICML 2026 · 0 citations)
Earliest papers here
- Successor Features for Transfer in Reinforcement Learning (NeurIPS 2017 · 183 citations)
- A neurally plausible model learns successor representations in partially observable environments (NeurIPS 2019 · 26 citations)
- Risk-Aware Transfer in Reinforcement Learning using Successor Features (NeurIPS 2021 · 8 citations)
What has been tried
11 papers here report 77 measured changes (a component added, removed or swapped, compared with a baseline): helped 64%, no clear effect 4%, hurt 29%, mixed 4%. Papers mostly publish changes that helped, so "helped" is over-represented.
Recent preprints
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Who publishes on it
Institutions on the most papers of this approach since 2023 (of 5 papers; from OpenAlex and the papers' affiliations).
- Google · 2 papers
- Google DeepMind · 2 papers
Use it
Download BibTeX of 12 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Learned state and action representations (148 papers)
- Robust visual encoders for RL (86 papers)
- Latent-space world models (83 papers)
Elsewhere in the topic
- In-context and adaptive RL (202 papers)
- Scalable RL systems and environments (179 papers)
- Probabilistic and autonomous RL (177 papers)
- RL for combinatorial routing (158 papers)
- Dexterous manipulation transfer (141 papers)
- RL with tree search for discovery (121 papers)
- Inferring decision-making from behavior (93 papers)
- Skill learning and diffusion policies (86 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).