Research map / Reinforcement learning
MDPs and planning: research map
682 accepted papers on MDPs and planning in Reinforcement learning, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 8 approaches. The busiest year so far is 2024.
Within Reinforcement learning, its share shrank from 12.9% in 2023–24 to 7.6% in 2025–26 (203 → 175 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
markov decision · processes · observable · 389 papers
Approaches in this cluster:
- Average-reward and regularized MDPs (135 papers)
Develop theory for regularized, average-reward, and non-stationary MDPs. - POMDPs and confounded RL (99 papers)
Study RL under partial observability and confounding. - Robust MDP policy gradient (87 papers)
Optimize policies in robust MDPs with rectangular uncertainty. - POMDP policy optimization (68 papers)
Solve POMDPs with Monte Carlo, transformers, and policy-based methods.
Most cited and most cited since 2024:
- Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning (CVPR 2020 · 229 citations)
- A Theory of Regularized Markov Decision Processes (ICML 2019 · 116 citations)
- Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach (AAAI 2025 · 5 citations)
- Principal-Agent Reward Shaping in MDPs (AAAI 2024 · 5 citations)
sample complexity · function approximation · epsilon · 293 papers
Approaches in this cluster:
- Function approximation in RL theory (93 papers)
Establish sample-efficient RL with general and low-rank function approximation. - Offline RL sample complexity (86 papers)
Prove near-optimal offline RL guarantees with pessimism and variance reduction. - Policy optimization theory (61 papers)
Analyze policy finetuning, reward-free exploration, and effective horizon. - Rich-observation exploration (53 papers)
Explore provably with state abstraction in rich-observation and model-based RL.
Most cited and most cited since 2024:
- Is Q-Learning Provably Efficient? (NeurIPS 2018 · 306 citations)
- Contextual Decision Processes with low Bellman rank are PAC-Learnable (ICML 2017 · 143 citations)
- Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL (NeurIPS 2024 · 11 citations)
- Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo (ICLR 2024 · 5 citations)
Related topics in Reinforcement learning
- Visual and representation RL (2,133)
- Policy gradient and actor-critic (1,502)
- Imitation learning (431)
- RL from feedback (737)
- Offline RL (623)