mlsanityData to 14 Oct 2026

LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

arXiv 2310.03342 ↗First posted 5 Oct 2023cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

In this paper, a unified framework for exploration in reinforcement learning (RL) is proposed based on an option-critic model. The proposed framework learns to integrate a set of diverse exploration strategies so that the agent can adaptively select the most effective exploration strategy over time to realize a relevant exploration-exploitation trade-off for each given task. The effectiveness of the proposed exploration framework is demonstrated by various experiments in the MiniGrid and Atari environments.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

22 of 34 references.

References · 22

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  5. 2018Exploration by Random Network Distillationcited by 993
  6. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 669
  7. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 639
  8. 2016Deep Exploration via Bootstrapped DQNcited by 527
  9. 2016The Option-Critic Architecturecited by 448
  10. 2017Population Based Training of Neural Networkscited by 367
  11. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 303
  12. 2017Count-Based Exploration with Neural Density Modelscited by 257
10 more
  1. 2015Incentivizing Exploration In Reinforcement Learning With Deep Predictive Modelscited by 154
  2. 2017Surprise-Based Intrinsic Motivation for Deep Reinforcement Learningcited by 83
  3. 2020Temporally-Extended ε-Greedy Explorationcited by 29
  4. 2021A Max-Min Entropy Framework for Reinforcement Learningcited by 28
  5. 2021Flexible Option Learningcited by 25
  6. 2020Population-Guided Parallel Policy Search for Reinforcement Learningcited by 21
  7. 2020Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Explorationcited by 20
  8. 2021TAAC: Temporally Abstract Actor-Critic for Continuous Controlcited by 15
  9. 2023A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learningcited by 5
  10. 2020ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learningcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

  1. 2024Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applicationscited by 38
  2. 2024The Exploration-Exploitation Dilemma Revisited: An Entropy Perspectivecited by 5
  3. 2025β-DQN: Improving Deep Q-Learning By Evolving the Behaviorcited by 3
  4. 2025OptionZero: Planning with Learned Optionscited by 2
  5. 2025D3HRL: A Distributed Hierarchical Reinforcement Learning Approach Based on Causal Discovery and Spurious Correlation Detectioncited by 1
  6. 2024Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learningcited by 0
  7. 2026Delay-Empowered Causal Hierarchical Reinforcement Learningcited by 0