mlsanityData to 7 Oct 2026

Exploration by Distributional Reinforcement Learning

arXiv 1805.01907 ↗First posted 4 May 2018cs.LG cs.AI stat.MLcited by 13

Not on the map. Why?

Abstract (arXiv)

We propose a framework based on distributional reinforcement learning and recent attempts to combine Bayesian parameter updates with deep reinforcement learning. We show that our proposed framework conceptually unifies multiple previous methods in exploration. We also derive a practical algorithm that achieves efficient exploration on challenging control tasks.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 May 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 1; 2025: 2; 2026: 1.

13 papers.

Sort:

  1. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  2. 2021SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learningcited by 15
  3. 2021Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learningcited by 4
  4. 2023Exploration via Epistemic Value Estimationcited by 4
  5. 2021Bayesian Distributional Policy Gradientscited by 3
  6. 2023Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterioncited by 3
  7. 2024A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertaintycited by 2
  8. 2025Stochastic Path Planning in Correlated Obstacle Fieldscited by 1
  9. 2022Exploration with Multi-Sample Target Values for Distributional Reinforcement Learningcited by 0
  10. 2023Bag of Policies for Distributional Deep Explorationcited by 0
  11. 2023DRL-ORA: Distributional Reinforcement Learning with Online Risk Adaptioncited by 0
  12. 2025Optimal Transport-Guided Safety in Temporal Difference Reinforcement Learningcited by 0
1 more
  1. 2026Distributional Active Inferencecited by 0