mlsanityData to 7 Oct 2026

Random Latent Exploration for Deep Reinforcement Learning

arXiv 2407.13755 ↗First posted 18 Jul 2024cs.LGcited by 6

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Abstract (arXiv)

We introduce Random Latent Exploration (RLE), a simple yet effective exploration strategy in reinforcement learning (RL). On average, RLE outperforms noise-based methods, which perturb the agent's actions, and bonus-based exploration, which rewards the agent for attempting novel behaviors. The core idea of RLE is to encourage the agent to explore different parts of the environment by pursuing randomly sampled goals in a latent space. RLE is as simple as noise-based methods, as it avoids complex bonus calculations but retains the deep exploration benefits of bonus-based methods. Our experiments show that RLE improves performance on average in both discrete (e.g., Atari) and continuous control tasks (e.g., Isaac Gym), enhancing exploration while remaining a simple and general plug-in for existing RL algorithms. Project website and code: https://srinathm1359.github.io/random-latent-exploration

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 64 of its 86 references that are in the map. First posted on arXiv 18 Jul 2024.

Builds on

64 of 86 references.

References · 64

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  6. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
  7. 2018Exploration by Random Network Distillationcited by 966
  8. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 882
  9. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 781
  10. 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 734
  11. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 632
  12. 2016Deep Exploration via Bootstrapped DQNcited by 518
38 more
  1. 2019Provably Efficient Reinforcement Learning with Linear Function Approximationcited by 514
  2. 2017A Tutorial on Thompson Samplingcited by 420
  3. 2016Reinforcement Learning with Unsupervised Auxiliary Taskscited by 400
  4. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 300
  5. 2018Large-Scale Study of Curiosity-Driven Learningcited by 277
  6. 2019Self-Supervised Exploration via Disagreementcited by 263
  7. 2017Noisy Networks for Explorationcited by 262
  8. 2020First return, then explorecited by 256
  9. 2017Count-Based Exploration with Neural Density Modelscited by 255
  10. 2018Visual Reinforcement Learning with Imagined Goalscited by 248
  11. 2018Provably Efficient Maximum Entropy Explorationcited by 240
  12. 2013(More) Efficient Reinforcement Learning via Posterior Samplingcited by 233
  13. 2020Model-Based Reinforcement Learning with Value-Targeted Regressioncited by 233
  14. 2021Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithmscited by 222
  15. 2021The Statistical Complexity of Interactive Decision Makingcited by 208
  16. 2019Go-Explore: a New Approach for Hard-Exploration Problemscited by 207
  17. 2021Bilinear Classes: A Structural Framework for Provable Generalization in RLcited by 176
  18. 2021Maximum Entropy RL (Provably) Solves Some Robust RL Problemscited by 169
  19. 2017Parameter Space Noise for Explorationcited by 158
  20. 2017Deep Exploration via Randomized Value Functionscited by 155
  21. 2017Neural Episodic Controlcited by 148
  22. 2023Extreme Q-Learning: MaxEnt RL without Entropycited by 113
  23. 2014Generalization and Exploration via Randomized Value Functionscited by 112
  24. 2019Frequentist Regret Bounds for Randomized Least-Squares Value Iterationcited by 100
  25. 2020PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learningcited by 91
  26. 2017Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learningcited by 79
  27. 2019Randomized Exploration in Generalized Linear Banditscited by 70
  28. 2017Boltzmann Exploration Done Rightcited by 69
  29. 2021A Survey of Exploration Methods in Reinforcement Learningcited by 68
  30. 2018Diversity-Driven Exploration Strategy for Deep Reinforcement Learningcited by 65
  31. 2021Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learningcited by 65
  32. 2021Meta-Thompson Samplingcited by 62
  33. 2020The Value-Improvement Path: Towards Better Representations for Reinforcement Learningcited by 60
  34. 2021On The Effect of Auxiliary Tasks on Representation Dynamicscited by 56
  35. 2021Randomized Exploration for Reinforcement Learning with General Value Function Approximationcited by 49
  36. 2022Making Linear MDPs Practical via Contrastive Representation Learningcited by 49
  37. 2023Making RL with Preference-based Feedback Efficient via Randomizationcited by 46
  38. 2017EX2: Exploration with Exemplar Models for Deep Reinforcement Learningcited by 42

50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2024Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learningcited by 21
  2. 2026Vector Policy Optimization: Training for Diversity Improves Test-Time Searchcited by 8
  3. 2025Imagine, Verify, Execute: Memory-guided Agentic Exploration with Vision-Language Modelscited by 2
  4. 2025Finite-time Convergence Analysis of Actor-Critic with Evolving Rewardcited by 0
  5. 2026Rethinking Refinement: Correcting Generative Bias without Noise Injectioncited by 0
  6. 2026Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Controlcited by 0