Research map / Reinforcement learning / MDPs and planning
Rich-observation exploration
Explore provably with state abstraction in rich-observation and model-based RL.
53 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in MDPs and planning, keyword cluster “sample complexity · function approximation · epsilon”
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.
15 papers in 2023–24 and 9 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 7.4% to 5.1%.
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
- 2016 · Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates (AAAI 2016 · 11 citations)
- 2017 · Contextual Decision Processes with low Bellman rank are PAC-Learnable (ICML 2017 · 143 citations)
- 2018 · TRUNCATED HORIZON POLICY SEARCH: COMBINING REINFORCEMENT LEARNING & IMITATION LEARNING (ICLR 2018 · 34 citations)
- 2019 · Provably Efficient Maximum Entropy Exploration (ICML 2019 · 97 citations)
- 2020 · Count-Based Exploration with the Successor Representation (AAAI 2020 · 103 citations)
- 2021 · Quantum algorithms for reinforcement learning with a generative model (ICML 2021 · 10 citations)
- 2022 · Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation (ICML 2022 · 26 citations)
- 2023 · Replicability in Reinforcement Learning (NeurIPS 2023 · 2 citations)
- 2024 · Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping (ICLR 2024 · 1 citation)
- 2025 · Stochastic Principal-Agent Problems: Computing and Learning Optimal History-Dependent Policies (NeurIPS 2025 · 0 citations)
- 2026 · From Generative to Episodic: Sample-Efficient Replicable Reinforcement Learning (ICML 2026 · 0 citations)
Earliest papers here
- Efficient PAC-Optimal Exploration in Concurrent, Continuous State MDPs with Delayed Updates (AAAI 2016 · 11 citations)
- PAC Reinforcement Learning with Rich Observations (NeurIPS 2016 · 9 citations)
- Only H is left: Near-tight Episodic PAC RL (NeurIPS 2016)
What has been tried
38 papers here report 218 measured changes (a component added, removed or swapped, compared with a baseline): helped 61%, no clear effect 14%, hurt 16%, mixed 9%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| use momentum coefficient β = 0.1 instead of β = 1.0 | Random MDPs, κ = 0 · average performance | 6.965 (± 0.07) -> 8.013 (± 0.07) | Helped | Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments (ICML 2024 · 1 citation) |
Recent preprints
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Who publishes on it
Institutions on the most papers of this approach since 2023 (of 24 papers; from OpenAlex and the papers' affiliations).
- Microsoft · 4 papers
- Massachusetts Institute of Technology · 3 papers
- Stanford University · 2 papers
- Carnegie Mellon University · 2 papers
- Yale University · 2 papers
Use it
Download BibTeX of 34 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Function approximation in RL theory (93 papers)
- Offline RL sample complexity (86 papers)
- Policy optimization theory (61 papers)
Elsewhere in the topic
- Average-reward and regularized MDPs (135 papers)
- POMDPs and confounded RL (99 papers)
- Robust MDP policy gradient (87 papers)
- POMDP policy optimization (68 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).