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arxiv: 2606.00950 · v1 · pith:NGNS6GGLnew · submitted 2026-05-31 · 💻 cs.LG

COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Space

Yao Luan , Ni Mu , Hanfei Ge , Yiqin Yang , Bo Xu , Qing-Shan Jia This is my paper

Pith reviewed 2026-06-28 17:54 UTC · model grok-4.3

classification 💻 cs.LG
keywords unsupervised skill discoveryguided skill discoverylatent spacehuman feedbackreinforcement learningskill learningsemantic coherence
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The pith

COLLIE constructs a semantically coherent skill latent space from dense unsupervised data to enable training-free guidance from sparse human feedback.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to solve the problem of unsupervised skill discovery producing irrelevant or dangerous behaviors by introducing guidance from human intent. Existing guided methods require extra training or dense feedback that is impractical. COLLIE instead uses abundant unsupervised data to build a latent space where skills are organized semantically, allowing guidance signals to be computed directly without training new models. This leads to skills that match human preferences, avoid hazards, and perform better on downstream tasks using only minimal feedback. A sympathetic reader would care because it reduces the human effort needed to steer reinforcement learning agents toward useful behaviors.

Core claim

COLLIE shows that a latent space learned from dense unsupervised data exhibits semantic coherence, which permits the construction of effective guidance signals directly from the space geometry using only sparse online human feedback, without requiring additional model training or pre-defined rules.

What carries the argument

The semantically coherent skill latent space built from unsupervised data, whose structure allows training-free guidance signals based on proximity to human-preferred directions.

If this is right

  • Learns diverse skills that align with human intent rather than uniform exploration.
  • Avoids hazardous behaviors that arise in unguided discovery.
  • Achieves superior performance on downstream tasks with minimal human feedback.
  • Applies to both state-based and pixel-based environments.
  • Theoretical analysis supports the reliability of the training-free guidance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Such a space might generalize to other unsupervised representation learning problems where structure enables cheap alignment.
  • Future work could test if the coherence property holds across different unsupervised pretraining methods.
  • Downstream tasks might include real-world robotics where safety is critical and feedback is costly.

Load-bearing premise

The dense unsupervised data produces a latent space whose geometry aligns with human semantic judgments in a way that makes simple distance-based guidance effective.

What would settle it

A controlled test where human raters evaluate the skills produced by COLLIE's guidance signals and find them no more aligned or safe than those from uniform exploration methods.

Figures

Figures reproduced from arXiv: 2606.00950 by Bo Xu, Hanfei Ge, Ni Mu, Qing-Shan Jia, Yao Luan, Yiqin Yang.

Figure 1
Figure 1. Figure 1: Overview of COLLIE. (1) We leverage dense unsupervised data to construct a semantically coherent latent space, where states with nearby embeddings share similar human desirability. (2) Using sparse human “good/bad” labels on states, COLLIE constructs a dense, training-free guidance signal w(s). ditional model training. To preserve the guidance signal’s accuracy, which requires labeled states to sufficientl… view at source ↗
Figure 2
Figure 2. Figure 2: Visualizations of skills learned by COLLIE and baseline methods, by plotting x-y (or x) trajectories sampled from the learned policies. Different colors represent distinct skills z. In task illustrations, human-undesirable regions (y = 0) are highlighted in red, desirable regions (y = 2) in yellow, and neutral regions in green. COLLIE effectively aligns diverse skills with human intent. Specifically, this … view at source ↗
Figure 3
Figure 3. Figure 3: An overview of COLLIE. COLLIE performs skill discovery and guidance signal construction simultaneously. The guidance signal is constructed by leveraging both rich unsupervised information from the learned skill latent space and human feedback on labeled states, without relying on expert-level data. We illustrate the full process of COLLIE in Algorithm 1 and [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: , COLLIE remains robust under noisy labels, indicating the reliability of our training-free guidance mechanism. (a) North, Rerror = 0.5. (b) North, Rerror = 1. (c) Range, Rerror = 0.5. (d) Range, Rerror = 1 [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualizations of skills learned by COLLIE with varying feedback number Ntotal. Ablation on query selection methods. To evaluate the effectiveness of COLLIE’s query selection method, we compare it with three baselines: a uniform sampling method (Uniform), an uncertainty-focused method (Uncertainty) that prioritizes states with large guidance signal entropy: Hw(s) = H [PITH_FULL_IMAGE:figures/full_fig_p017… view at source ↗
Figure 6
Figure 6. Figure 6: Visualizations of skills learned by COLLIE with varying segment length H. Evaluation under noisy observation. To evaluate COLLIE’s robustness to state observation noise, we add Gaussian noise to the observed state: for an original state s, the observed input is sˆ = (1 + x) · s, where x ∼ N (0, 0.01I), and N denotes the Gaussian distribution. As shown in the table below, the performance degrades only sligh… view at source ↗
Figure 7
Figure 7. Figure 7: Visualizations of skills learned by COLLIE with real human labelers on the Ant Range task. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visualization of the reason for the dead zone phenomenon in scenarios with obstacles (Hole as an example). Human-undesirable regions are highlighted in red, and other regions are highlighted in green. In the right subfigure, gray regions represent “dead zones”. We use [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visualizations of skills learned by COLLIE using 4-dim skills. F. Extended Related Work Unsupervised reinforcement learning. Unsupervised reinforcement learning (Xie et al., 2022) learns a policy or set of policies with unlabeled data (transitions without task-specific rewards) to explore the state space. The target is to acquire knowledge of the environment, thereby facilitating downstream tasks. To encou… view at source ↗
Figure 10
Figure 10. Figure 10 [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
read the original abstract

Unsupervised skill discovery (USD) aims to learn diverse behaviors without reward functions, but often results in task-irrelevant or hazardous behaviors due to uniform exploration. Guided skill discovery (GSD) addresses this issue by incorporating human intent to focus exploration on meaningful regions. However, existing GSD methods typically require training additional guidance models, and rely on pre-defined rules or expert demonstration, which can be ineffective under sparse, online-collected human feedback. To overcome this, we propose COLLIE, a GSD framework that leverages dense unsupervised data to construct a semantically coherent skill latent space. This latent space is well-structured, enabling reliable guidance with sparse online feedback. Moreover, its semantic coherence property enables training-free construction of guidance signals, eliminating the need for additional model training beyond skill learning. Theoretical analysis justifies the effectiveness of our training-free guidance signal, while experiments across diverse state-based and pixel-based tasks show that COLLIE learns diverse, human-aligned skills, avoids hazardous behaviors, and achieves superior downstream performance with minimal human feedback.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. COLLIE proposes a guided skill discovery (GSD) framework that leverages dense unsupervised data to construct a semantically coherent skill latent space. This space enables reliable, training-free guidance signals from sparse online human feedback, eliminating the need for additional guidance model training. Theoretical analysis is claimed to justify the guidance signal, and experiments on state-based and pixel-based tasks are said to show that COLLIE learns diverse human-aligned skills, avoids hazards, and yields superior downstream performance with minimal feedback.

Significance. If the semantic coherence property and training-free guidance hold, the approach could make guided skill discovery more practical by minimizing reliance on dense human feedback and extra model training, advancing unsupervised RL toward safer, more intent-aligned behaviors.

major comments (2)
  1. [Abstract and theoretical analysis section] Abstract and theoretical analysis section: The load-bearing claim that dense unsupervised data alone yields a 'semantically coherent' latent space whose geometry supports training-free guidance aligned with human intent is not secured by the construction (contrastive or reconstruction objectives on trajectories). No inductive bias is shown to ensure discovered axes match human-meaningful factors rather than spurious correlations, so the theoretical justification inherits the same unverified alignment assumption.
  2. [Experiments section] Experiments section: Claims of avoiding hazardous behaviors and superior downstream performance with minimal feedback rest on the latent space reliably aligning with human intent; without explicit verification that the unsupervised structure does not encode task-irrelevant invariances, the cross-task results do not yet substantiate the central GSD advantage.
minor comments (2)
  1. [Notation and method description] Clarify notation for the skill latent space and the exact form of the training-free guidance signal (e.g., how sparse feedback is mapped without additional fitting).
  2. [Experiments] Add a table or figure comparing the number of human feedback queries required versus prior GSD baselines to quantify the 'minimal' claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below, providing clarifications on the theoretical assumptions and experimental validations while noting where revisions can strengthen the presentation.

read point-by-point responses

  1. Referee: [Abstract and theoretical analysis section] Abstract and theoretical analysis section: The load-bearing claim that dense unsupervised data alone yields a 'semantically coherent' latent space whose geometry supports training-free guidance aligned with human intent is not secured by the construction (contrastive or reconstruction objectives on trajectories). No inductive bias is shown to ensure discovered axes match human-meaningful factors rather than spurious correlations, so the theoretical justification inherits the same unverified alignment assumption.

    Authors: The theoretical analysis in the manuscript defines semantic coherence as the property that the latent space geometry permits reliable training-free guidance from sparse feedback, and derives that the guidance signal is effective under this property when the unsupervised objectives (contrastive and reconstruction on trajectories) produce a well-structured space. The construction does not include an explicit inductive bias guaranteeing human-meaningful factors; instead, it relies on the empirical observation that diverse unsupervised data from the environments implicitly encodes such structure. We agree this assumption merits clearer qualification in the abstract and theory section to avoid overstatement, and will revise accordingly to emphasize the conditional nature of the theoretical result. revision: partial

  2. Referee: [Experiments section] Experiments section: Claims of avoiding hazardous behaviors and superior downstream performance with minimal feedback rest on the latent space reliably aligning with human intent; without explicit verification that the unsupervised structure does not encode task-irrelevant invariances, the cross-task results do not yet substantiate the central GSD advantage.

    Authors: The experiments demonstrate the central GSD advantage through quantitative metrics (diversity, hazard avoidance rates, and downstream task returns) and qualitative visualizations across state-based and pixel-based environments, where COLLIE with minimal feedback outperforms baselines that lack the coherent latent space. Cross-task transfer results further support that the space generalizes beyond training distributions. However, we acknowledge the absence of an explicit analysis (e.g., correlation of latent dimensions with known semantic factors or invariance checks) to rule out task-irrelevant encodings. We will add such verification in a revised experiments section to more directly substantiate the alignment claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation remains self-contained

full rationale

The COLLIE framework constructs the skill latent space directly from unsupervised trajectory data via standard contrastive/reconstruction objectives, then uses its geometry for training-free guidance whose justification is supplied by an internal theoretical analysis. No quoted equations reduce a claimed prediction or uniqueness result to a fitted parameter or self-citation chain; the alignment with human intent is presented as an empirical and theoretical property of the learned space rather than a definitional tautology. The derivation therefore does not collapse to its inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No full manuscript text available; cannot extract specific free parameters, axioms, or invented entities from the abstract alone.

pith-pipeline@v0.9.1-grok · 5717 in / 893 out tokens · 22705 ms · 2026-06-28T17:54:49.748334+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

57 extracted references · 8 canonical work pages · 3 internal anchors

  1. [1]

    Programming by feedback

    Akrour, R., Schoenauer, M., Sebag, M., and Souplet, J.-C. Programming by feedback. In International Conference on Machine Learning, volume 32, pp.\ 1503--1511. PMLR, 2014

    2014

  2. [2]

    Unifying count-based exploration and intrinsic motivation

    Bellemare, M., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., and Munos, R. Unifying count-based exploration and intrinsic motivation. Advances in neural information processing systems, 29, 2016

    2016

  3. [3]

    Boyd, S. P. and Vandenberghe, L. Convex optimization. Cambridge University Press, 2004

    2004

  4. [4]

    OpenAI Gym

    Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. Openai gym. arXiv preprint arXiv:1606.01540, 2016

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  5. [5]

    D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

    Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. Language models are few-shot learners. Advances in neural information processing systems, 33: 0 1877--1901, 2020

    1901

  6. [6]

    Exploration by random network distillation

    Burda, Y., Edwards, H., Storkey, A., and Klimov, O. Exploration by random network distillation. In Seventh International Conference on Learning Representations, pp.\ 1--17, 2019

    2019

  7. [7]

    Explore, discover and learn: Unsupervised discovery of state-covering skills

    Campos, V., Trott, A., Xiong, C., Socher, R., Gir \'o -i Nieto, X., and Torres, J. Explore, discover and learn: Unsupervised discovery of state-covering skills. In International conference on machine learning, pp.\ 1317--1327. PMLR, 2020

    2020

  8. [8]

    A simple framework for contrastive learning of visual representations

    Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pp.\ 1597--1607. PmLR, 2020

    2020

  9. [9]

    Rime: Robust preference-based reinforcement learning with noisy preferences

    Cheng, J., Xiong, G., Dai, X., Miao, Q., Lv, Y., and Wang, F.-Y. Rime: Robust preference-based reinforcement learning with noisy preferences. In International Conference on Machine Learning, pp.\ 8229--8247. PMLR, 2024

    2024

  10. [10]

    Listwise reward estimation for offline preference-based reinforcement learning

    Choi, H., Jung, S., Ahn, H., and Moon, T. Listwise reward estimation for offline preference-based reinforcement learning. In International Conference on Machine Learning, pp.\ 8651--8671. PMLR, 2024

    2024

  11. [11]

    F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D

    Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30, 2017

    2017

  12. [12]

    and Hart, P

    Cover, T. and Hart, P. Nearest neighbor pattern classification. IEEE transactions on information theory, 13 0 (1): 0 21--27, 1967

    1967

  13. [13]

    Active reward learning

    Daniel, C., Viering, M., Metz, J., Kroemer, O., and Peters, J. Active reward learning. In Robotics: Science and Systems X. Robotics: Science and Systems (RSS-2014), July 12-16, Berkeley, California, United States, 2014. doi:10.15607/RSS.2014.X.031

    work page doi:10.15607/rss.2014.x.031 2014

  14. [14]

    BERT : Pre-training of deep bidirectional transformers for language understanding

    Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. BERT : Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pp.\ 4171--4186, 2019

    2019

  15. [15]

    Guiding pretraining in reinforcement learning with large language models

    Du, Y., Watkins, O., Wang, Z., Colas, C., Darrell, T., Abbeel, P., Gupta, A., and Andreas, J. Guiding pretraining in reinforcement learning with large language models. In International Conference on Machine Learning, pp.\ 8657--8677. PMLR, 2023

    2023

  16. [16]

    Adversarial intrinsic motivation for reinforcement learning

    Durugkar, I., Tec, M., Niekum, S., and Stone, P. Adversarial intrinsic motivation for reinforcement learning. Advances in Neural Information Processing Systems, 34: 0 8622--8636, 2021

    2021

  17. [17]

    Diversity is all you need: Learning skills without a reward function

    Eysenbach, B., Ibarz, J., Gupta, A., and Levine, S. Diversity is all you need: Learning skills without a reward function. In 7th International Conference on Learning Representations, ICLR 2019, 2019

    2019

  18. [18]

    Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

    Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Dy, J. and Krause, A. (eds.), Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pp.\ 1861--1870. PMLR, 10--15 Jul 2018

    2018

  19. [19]

    and Shad, R

    Hamedi, H. and Shad, R. Context-aware similarity measurement of lane-changing trajectories. Expert Systems with Applications, 209: 0 118289, 2022

    2022

  20. [20]

    Dynamical distance learning for semi-supervised and unsupervised skill discovery

    Hartikainen, K., Geng, X., Haarnoja, T., and Levine, S. Dynamical distance learning for semi-supervised and unsupervised skill discovery. 2019

    2019

  21. [21]

    Provably efficient maximum entropy exploration

    Hazan, E., Kakade, S., Singh, K., and Van Soest, A. Provably efficient maximum entropy exploration. In International Conference on Machine Learning, pp.\ 2681--2691. PMLR, 2019

    2019

  22. [22]

    G., and Rana, S

    Hussonnois, M., Karimpanal, T. G., and Rana, S. Controlled diversity with preference: Towards learning a diverse set of desired skills. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, pp.\ 1135--1143, 2023

    2023

  23. [23]

    G., and Rana, S

    Hussonnois, M., Karimpanal, T. G., and Rana, S. Human-aligned skill discovery: Balancing behaviour exploration and alignment. In Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, pp.\ 1025--1033, 2025

    2025

  24. [24]

    Episodic novelty through temporal distance

    Jiang, Y., Liu, Q., Yang, Y., Ma, X., Zhong, D., Hu, H., Yang, J., Liang, B., XU, B., Zhang, C., et al. Episodic novelty through temporal distance. In The Thirteenth International Conference on Learning Representations, 2025

    2025

  25. [25]

    Kaelbling, L. P. Learning to achieve goals. In IJCAI, volume 2, pp.\ 1094--8, 1993

    1993

  26. [26]

    K., Lee, H., Hwang, D., Kim, D., and Choo, J

    Kim, H., LEE, B. K., Lee, H., Hwang, D., Kim, D., and Choo, J. Do's and don'ts: Learning desirable skills with instruction videos. Advances in Neural Information Processing Systems, 37: 0 47741--47766, 2024

    2024

  27. [27]

    Safety-aware unsupervised skill discovery

    Kim, S., Kwon, J., Lee, T., Park, Y., and Perez, J. Safety-aware unsupervised skill discovery. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp.\ 894--900. IEEE, 2023

    2023

  28. [28]

    Kingma, D. P. and Ba, J. Adam: A method for stochastic optimization. International Conference on Learning Representations, 2015

    2015

  29. [29]

    Learning task agnostic skills with data-driven guidance

    Klemsdal, E., Herland, S., and Murad, A. Learning task agnostic skills with data-driven guidance. In ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021. URL https://openreview.net/forum?id=CPh9DeHv08U

    2021

  30. [30]

    B., Yarats, D., Rajeswaran, A., and Abbeel, P

    Laskin, M., Liu, H., Peng, X. B., Yarats, D., Rajeswaran, A., and Abbeel, P. Unsupervised reinforcement learning with contrastive intrinsic control. Advances in Neural Information Processing Systems, 35: 0 34478--34491, 2022

    2022

  31. [31]

    S., et al

    LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1989. doi:10.1162/neco.1989.1.4.541

    work page doi:10.1162/neco.1989.1.4.541 1989

  32. [32]

    M., and Abbeel, P

    Lee, K., Smith, L. M., and Abbeel, P. Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training. In International Conference on Machine Learning, pp.\ 6152--6163. PMLR, 2021

    2021

  33. [33]

    and Abbeel, P

    Liu, H. and Abbeel, P. Aps: Active pretraining with successor features. In International Conference on Machine Learning, pp.\ 6736--6747. PMLR, 2021 a

    2021

  34. [34]

    and Abbeel, P

    Liu, H. and Abbeel, P. Behavior from the void: Unsupervised active pre-training. Advances in Neural Information Processing Systems, 34: 0 18459--18473, 2021 b

    2021

  35. [35]

    STAIR : A ddressing S tage M isalignment through T emporal- A ligned P reference R einforcement L earning

    Luan, Y., Mu, N., Yang, Y., Xu, B., and Jia, Q.-S. STAIR : A ddressing S tage M isalignment through T emporal- A ligned P reference R einforcement L earning. In The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025

    2025

  36. [36]

    CLARIFY : C ontrastive P reference R einforcement L earning for U ntangling A mbiguous Q ueries

    Mu, N., Hu, H., Hu, X., Yang, Y., Xu, B., and Jia, Q.-S. CLARIFY : C ontrastive P reference R einforcement L earning for U ntangling A mbiguous Q ueries. In Forty-second International Conference on Machine Learning, 2025 a

    2025

  37. [37]

    P reference-based M ulti- O bjective R einforcement L earning

    Mu, N., Luan, Y., and Jia, Q.-S. P reference-based M ulti- O bjective R einforcement L earning. IEEE Transactions on Automation Science and Engineering, 22: 0 18737--18749, 2025 b . doi:10.1109/TASE.2025.3589271

    work page doi:10.1109/tase.2025.3589271 2025

  38. [38]

    S - EPOA : O vercoming the I ndistinguishability of S egments with S kill- D riven P reference- B ased R einforcement L earning

    Mu, N., Luan, Y., Yang, Y., Xu, B., and Jia, Q.-s. S - EPOA : O vercoming the I ndistinguishability of S egments with S kill- D riven P reference- B ased R einforcement L earning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 , pp.\ 5967--5975, 2025 c . doi:10.24963/ijcai.2025/664

    work page doi:10.24963/ijcai.2025/664 2025

  39. [39]

    G., Oord, A., and Munos, R

    Ostrovski, G., Bellemare, M. G., Oord, A., and Munos, R. Count-based exploration with neural density models. In International conference on machine learning, pp.\ 2721--2730. PMLR, 2017

    2017

  40. [40]

    Surf: Semi-supervised reward learning with data augmentation for feedback-efficient preference-based reinforcement learning

    Park, J., Seo, Y., Shin, J., Lee, H., Abbeel, P., and Lee, K. Surf: Semi-supervised reward learning with data augmentation for feedback-efficient preference-based reinforcement learning. In 10th International Conference on Learning Representations, ICLR 2022, 2022 a

    2022

  41. [41]

    Lipschitz-constrained unsupervised skill discovery

    Park, S., Choi, J., Kim, J., Lee, H., and Kim, G. Lipschitz-constrained unsupervised skill discovery. In International Conference on Learning Representations, 2022 b

    2022

  42. [42]

    Controllability-aware unsupervised skill discovery

    Park, S., Lee, K., Lee, Y., and Abbeel, P. Controllability-aware unsupervised skill discovery. In Proceedings of the 40th International Conference on Machine Learning, pp.\ 27225--27245, 2023

    2023

  43. [43]

    Metra: Scalable unsupervised rl with metric-aware abstraction

    Park, S., Rybkin, O., and Levine, S. Metra: Scalable unsupervised rl with metric-aware abstraction. In International Conference on Learning Representations, 2024

    2024

  44. [44]

    A., and Darrell, T

    Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T. Curiosity-driven exploration by self-supervised prediction. In International conference on machine learning, pp.\ 2778--2787. PMLR, 2017

    2017

  45. [45]

    W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

    Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp.\ 8748--8763. PmLR, 2021

    2021

  46. [46]

    Benchmarking Safe Exploration in Deep Reinforcement Learning , 2019

    Ray, A., Achiam, J., and Amodei, D. Benchmarking Safe Exploration in Deep Reinforcement Learning , 2019. URL https://openai.com/index/benchmarking-safe-exploration-in-deep-reinforcement-learning/

    2019

  47. [47]

    Proximal Policy Optimization Algorithms

    Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  48. [48]

    Dynamics-aware unsupervised discovery of skills

    Sharma, A., Gu, S., Levine, S., Kumar, V., and Hausman, K. Dynamics-aware unsupervised discovery of skills. In International Conference on Learning Representations, 2020

    2020

  49. [49]

    D., and Brown, D

    Shin, D., Dragan, A. D., and Brown, D. S. Benchmarks and algorithms for offline preference-based reward learning. Transactions on Machine Learning Research, 2023

    2023

  50. [50]

    Nearest neighbor estimates of entropy

    Singh, H., Misra, N., Hnizdo, V., Fedorowicz, A., and Demchuk, E. Nearest neighbor estimates of entropy. American journal of mathematical and management sciences, 23 0 (3-4): 0 301--321, 2003

    2003

  51. [51]

    Preference-learning based inverse reinforcement learning for dialog control

    Sugiyama, H., Meguro, T., and Minami, Y. Preference-learning based inverse reinforcement learning for dialog control. In INTERSPEECH, volume 12, pp.\ 222--225, 2012

    2012

  52. [52]

    Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. d. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al. Deepmind control suite. arXiv preprint arXiv:1801.00690, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  53. [53]

    Mujoco: A physics engine for model-based control

    Todorov, E., Erez, T., and Tassa, Y. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pp.\ 5026--5033. IEEE, 2012

    2012

  54. [54]

    I., Liang, P., and Manning, C

    Wang, S. I., Liang, P., and Manning, C. D. Learning language games through interaction. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.\ 2368--2378, 2016

    2016

  55. [55]

    Effective and efficient sports play retrieval with deep representation learning

    Wang, Z., Long, C., Cong, G., and Ju, C. Effective and efficient sports play retrieval with deep representation learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.\ 499--509, 2019

    2019

  56. [56]

    Pretraining in deep reinforcement learning: A survey

    Xie, Z., Lin, Z., Li, J., Li, S., and Ye, D. Pretraining in deep reinforcement learning: A survey. arXiv preprint arXiv:2211.03959, 2022

    work page arXiv 2022

  57. [57]

    Can a MISL fly? analysis and ingredients for mutual information skill learning

    Zheng, C., Tuyls, J., Peng, J., and Eysenbach, B. Can a MISL fly? analysis and ingredients for mutual information skill learning. In The Thirteenth International Conference on Learning Representations, 2025

    2025