Research map / Reinforcement learning
Visual and representation RL: research map
2,133 accepted papers on Visual and representation RL in Reinforcement learning, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 6 clusters and 23 approaches. The busiest year so far is 2026.
Within Reinforcement learning, its share shrank from 36.5% in 2023–24 to 30.0% in 2025–26 (572 → 696 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
search · decision · systems · 1,011 papers
Approaches in this cluster:
- In-context and adaptive RL (202 papers)
Enable fast adaptation in RL via in-context learning, memory, and domain transfer. - Scalable RL systems and environments (179 papers)
Scale RL training and benchmark environments. - Probabilistic and autonomous RL (177 papers)
Frame RL as inference and learn without episodic resets. - RL for combinatorial routing (158 papers)
Solve routing and NP-hard problems with learned RL policies and search. - RL with tree search for discovery (121 papers)
Search molecules and plans with RL and Monte Carlo tree search. - Inferring decision-making from behavior (93 papers)
Interpret human and animal decisions via inverse RL and policy recovery. - Curriculum and environment design (81 papers)
Generates training environments and curricula to improve agent generalization and skill acquisition.
Most cited and most cited since 2024:
- A Deep Reinforced Model for Abstractive Summarization (ICLR 2018 · 1,255 citations)
- Action-Decision Networks for Visual Tracking With Deep Reinforcement Learning (CVPR 2017 · 574 citations)
- Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning (AAAI 2024 · 41 citations)
- EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading (AAAI 2024 · 21 citations)
representation · latent · generalization · 329 papers
Approaches in this cluster:
- Learned state and action representations (148 papers)
Learn compact representations of states, actions, or contexts to speed up and generalize RL. - Robust visual encoders for RL (86 papers)
Make pixel-based RL generalize to visual perturbations via pretrained encoders, invariance, and attention. - Latent-space world models (83 papers)
Learn latent dynamics models and temporal-consistency objectives for model-based and transfer RL. - Successor features for transfer (12 papers)
Use successor representations and features to reuse value information across tasks and policies.
Most cited and most cited since 2024:
- Benchmarking Deep Reinforcement Learning for Continuous Control (ICML 2016 · 949 citations)
- Recurrent World Models Facilitate Policy Evolution (NeurIPS 2018 · 452 citations)
- Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment (CVPR 2024 · 20 citations)
- What Effects the Generalization in Visual Reinforcement Learning: Policy Consistency with Truncated Return Prediction (AAAI 2024 · 9 citations)
robot · manipulation · simulation · 271 papers
Approaches in this cluster:
- Skill learning and diffusion policies (86 papers)
Learn transferable skills and diffusion policies for continuous control. - Dexterous manipulation transfer (141 papers)
Transfer manipulation skills with residual learning, benchmarks, and cross-embodiment data. - Vision-based robot RL (44 papers)
Learn transferable visual control policies and world models for embodied agents.
Most cited and most cited since 2024:
- SAPIEN: A SimulAted Part-Based Interactive ENvironment (CVPR 2020 · 402 citations)
- Hindsight Experience Replay (NeurIPS 2017 · 337 citations)
- Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes (AAAI 2025 · 172 citations)
- Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation (CVPR 2024 · 54 citations)
exploration · intrinsic · skills · 265 papers
Approaches in this cluster:
- Novelty and impact-driven exploration (85 papers)
Reward visiting novel or impactful states, or diversify behavior, to explore better. - Hierarchical skills and priors (84 papers)
Learn reusable sub-policies and skill priors for hierarchical reinforcement learning. - Reward shaping for sparse rewards (51 papers)
Shape or learn intrinsic rewards and curricula to cope with sparse reward signals. - Unsupervised skill discovery (45 papers)
Discover diverse skills without reward using mutual-information and contrastive objectives.
Most cited and most cited since 2024:
- Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation (NeurIPS 2016 · 822 citations)
- Parameter Space Noise for Exploration (ICLR 2018 · 359 citations)
- Robust Policy Learning via Offline Skill Diffusion (AAAI 2024 · 6 citations)
- Bootstrapped Reward Shaping (AAAI 2025 · 5 citations)
goal · conditioned · hierarchical · 156 papers
Approaches in this cluster:
- Goal-conditioned policy learning (66 papers)
Train goal-reaching policies with learned goal representations, discriminative rewards, and pretraining. - Graph search and compositional goals (55 papers)
Plan over goal graphs or compositional goal abstractions to improve goal-conditioned RL. - Subgoal generation for hierarchical RL (35 papers)
Generate adjacency-constrained or probabilistic subgoals so high-level policies guide low-level control.
Most cited and most cited since 2024:
- Visual Reinforcement Learning with Imagined Goals (NeurIPS 2018 · 307 citations)
- Data-Efficient Hierarchical Reinforcement Learning (NeurIPS 2018 · 248 citations)
- Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning (AAAI 2024 · 3 citations)
- Goal Conditioned Reinforcement Learning for Photo Finishing Tuning (NeurIPS 2024 · 3 citations)
meta · adaptation · context · 101 papers
Approaches in this cluster:
- Task-inference meta-RL (57 papers)
Meta-train context encoders and task selection so agents infer new tasks and adapt quickly. - Task structure and curricula for meta-RL (44 papers)
Exploit subtask structure, curricula, and sampling choices to improve meta-RL adaptation.
Most cited and most cited since 2024:
- Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (ICML 2019 · 214 citations)
- Meta-Reinforcement Learning of Structured Exploration Strategies (NeurIPS 2018 · 174 citations)
- Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations (AAAI 2024 · 12 citations)
- Meta-Reinforcement Learning via Exploratory Task Clustering (AAAI 2024 · 7 citations)
Related topics in Reinforcement learning
- MDPs and planning (682)
- Policy gradient and actor-critic (1,502)
- Imitation learning (431)
- RL from feedback (737)
- Offline RL (623)