---
title: "Eureka — paper by Ma et al. (2023) — summary and ideas"
description: "The authors present Eureka, which uses a coding LLM (GPT-4) to write reward programs automatically. 58 references, cited by 94 papers here."
authors: ["Yecheng Jason Ma", "William Liang", "Guanzhi Wang", "De-An Huang", "Osbert Bastani", "Dinesh Jayaraman", "Yuke Zhu", "Linxi Fan", "Anima Anandkumar"]
year: 2023
venue: "arXiv:2310.12931"
source: "https://arxiv.org/abs/2310.12931"
doi: "10.48550/arxiv.2310.12931"
arxiv: "2310.12931"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ma-2023-eureka-human-level-reward-design/"
---
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# Eureka: Human-Level Reward Design via Coding Large Language Models

**Authors:** Yecheng Jason Ma, William Liang, [Guanzhi Wang](https://md.research.elamur.ai/free/en/authors/guanzhi-wang/), [De-An Huang](https://md.research.elamur.ai/free/en/authors/de-an-huang/), Osbert Bastani, Dinesh Jayaraman, [Yuke Zhu](https://md.research.elamur.ai/free/en/authors/yuke-zhu/), [Linxi Fan](https://md.research.elamur.ai/free/en/authors/linxi-fan/) and [Anima Anandkumar](https://md.research.elamur.ai/free/en/authors/anima-anandkumar/) · **Year:** 2023 · **Venue:** arXiv:2310.12931 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2310.12931) · [PDF](https://arxiv.org/pdf/2310.12931) · [DOI 10.48550/arxiv.2310.12931](https://doi.org/10.48550/arxiv.2310.12931) · [arXiv 2310.12931](https://arxiv.org/abs/2310.12931) · [OpenAlex](https://openalex.org/W4387839203)

**Citations:** 48 citations (OpenAlex, 2026-10-14) · [94](https://md.research.elamur.ai/free/en/papers/ma-2023-eureka-human-level-reward-design/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Hand-crafting reward functions for reinforcement learning is slow, relies on trial and error, and often yields sub-optimal results. The difficulty is greatest for low-level dexterous control, where LLMs had so far been of limited use. The authors present Eureka, which uses a coding LLM (GPT-4) to write reward programs automatically. The model receives the raw environment source code and a short task description, with no task-specific prompts, templates, or few-shot examples. It then improves its candidates through an evolutionary loop:

- it samples batches of reward functions;
- it trains policies on each one using GPU-accelerated RL in IsaacGym;
- it feeds back a textual "reward reflection" based on training statistics, so the next round refines the strongest candidate.

Across 29 environments with 10 robot morphologies, Eureka's rewards beat expert human rewards on most tasks. Combined with curriculum learning, they enabled pen spinning on a simulated Shadow Hand for the first time. The method also accepts human textual feedback, which gives a form of RLHF that needs no gradient updates. The work matters because it automates a key bottleneck of RL while still allowing human oversight.

## Key points
- On 29 open-source RL environments covering 10 morphologies (quadruped, quadcopter, biped, manipulator, dexterous hands), Eureka beats human-engineered rewards on 83% of tasks, with a 52% average normalized improvement.
- It clearly outperforms the earlier L2R approach, because it generates free-form reward code rather than filling templates.
- Sampling 16 candidates per iteration was enough to obtain at least one executable reward in every environment on the first round. IsaacGym speeds up policy evaluation by up to three orders of magnitude.
- The work is motivated by a survey (Booth et al., 2023): 92% of practitioners design rewards manually by trial and error, and 89% consider their rewards sub-optimal.
- The main limitations are:
  - evaluation is largely simulation-only, apart from a preliminary Sim2Real hopping test;
  - a human-definable fitness function F is required;
  - experiments use a single simulator and RL algorithm, with an exception for Mujoco Humanoid.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Pennsylvania](https://md.research.elamur.ai/free/en/universities/university-of-pennsylvania/) — University, Philadelphia, United States · 233 works · 65,583 citations
- [The University of Texas at Austin](https://md.research.elamur.ai/free/en/universities/the-university-of-texas-at-austin/) — University, Austin, United States · 119 works · 57,950 citations
- [California Institute of Technology](https://md.research.elamur.ai/free/en/universities/california-institute-of-technology/) — University, Pasadena, United States · 11 works · 1,138 citations

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🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works

## Cite

Cite as: Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan and Anima Anandkumar. 2023. Eureka: Human-Level Reward Design via Coding Large Language Models. arXiv:2310.12931.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{ma2023eureka,
  title = {{Eureka: Human-Level Reward Design via Coding Large Language Models}},
  author = {Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan and Anima Anandkumar},
  year = {2023},
  doi = {10.48550/arxiv.2310.12931},
  eprint = {2310.12931},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2310.12931}
}
```

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## References

58 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Pieter Abbeel and Andrew Y Ng. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the twenty-first international conference on Machine learning, pp. 1, 2004. [find](https://scholar.google.com/scholar?q=Pieter+Abbeel+and+Andrew+Y+Ng%2E+Apprenticeship+learning+via+inverse+reinforcement+learning%2E+In+Proceedings+of+the+twenty%2Dfirst+international+conference+on+Machine+learning%2C+pp%2E+1%2C+2004%2E)
2. Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare. [Deep reinforcement learning at the edge of the statistical precipice](https://md.research.elamur.ai/free/en/papers/agarwal-2021-deep-reinforcement-learning-edge-statistical/). Advances in neural information processing systems, 34:29304–29320, 2021.
3. Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. [Do as i can, not as i say: Grounding language in robotic affordances](https://md.research.elamur.ai/free/en/papers/ahn-2022-i-i-say-grounding-language/). arXiv preprint arXiv:2204.01691, 2022.
4. Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al. Solving rubik’s cube with a robot hand. arXiv preprint arXiv:1910.07113, 2019. [find](https://scholar.google.com/scholar?q=Ilge+Akkaya%2C+Marcin+Andrychowicz%2C+Maciek+Chociej%2C+Mateusz+Litwin%2C+Bob+McGrew%2C+Arthur+Petron%2C+Alex+Paino%2C+Matthias+Plappert%2C+Glenn+Powell%2C+Raphael+Ribas%2C+et+al%2E+Solving+rubik%E2%80%99s+cube+with+a+robot+hand%2E+)
5. OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al. Learning dexterous in-hand manipulation. The International Journal of Robotics Research, 39(1):3–20, 2020. [find](https://scholar.google.com/scholar?q=OpenAI%3A+Marcin+Andrychowicz%2C+Bowen+Baker%2C+Maciek+Chociej%2C+Rafal+Jozefowicz%2C+Bob+McGrew%2C+Jakub+Pachocki%2C+Arthur+Petron%2C+Matthias+Plappert%2C+Glenn+Powell%2C+Alex+Ray%2C+et+al%2E+Learning+dexterous+in%2Dhand+mani)
6. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. [Program synthesis with large language models](https://md.research.elamur.ai/free/en/papers/austin-2021-program-synthesis-large-language-models/). arXiv preprint arXiv:2108.07732, 2021.
7. Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. [Curriculum learning](https://md.research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/). In Proceedings of the 26th annual international conference on machine learning, pp. 41–48, 2009.
8. Serena Booth, W Bradley Knox, Julie Shah, Scott Niekum, Peter Stone, and Alessandro Allievi. The perils of trial-and-error reward design: misdesign through overfitting and invalid task specifications. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pp. 5920–5929, 2023. [find](https://scholar.google.com/scholar?q=Serena+Booth%2C+W+Bradley+Knox%2C+Julie+Shah%2C+Scott+Niekum%2C+Peter+Stone%2C+and+Alessandro+Allievi%2E+The+perils+of+trial%2Dand%2Derror+reward+design%3A+misdesign+through+overfitting+and+invalid+task+specifications%2E)
9. Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016. [find](https://scholar.google.com/scholar?q=Greg+Brockman%2C+Vicki+Cheung%2C+Ludwig+Pettersson%2C+Jonas+Schneider%2C+John+Schulman%2C+Jie+Tang%2C+and+Wojciech+Zaremba%2E+Openai+gym%2E+arXiv+preprint+arXiv%3A1606%2E01540%2C+2016%2E)
10. Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.15818, 2023. [find](https://scholar.google.com/scholar?q=Anthony+Brohan%2C+Noah+Brown%2C+Justice+Carbajal%2C+Yevgen+Chebotar%2C+Xi+Chen%2C+Krzysztof+Choromanski%2C+Tianli+Ding%2C+Danny+Driess%2C+Avinava+Dubey%2C+Chelsea+Finn%2C+et+al%2E+Rt%2D2%3A+Vision%2Dlanguage%2Daction+models+transf)
11. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. [Evaluating large language models trained on code](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/). arXiv preprint arXiv:2107.03374, 2021.
12. Yuanpei Chen, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuan Jiang, Zongqing Lu, Stephen McAleer, Hao Dong, Song-Chun Zhu, and Yaodong Yang. Towards human-level bimanual dexterous manipulation with reinforcement learning. Advances in Neural Information Processing Systems, 35:5150–5163, 2022. [find](https://scholar.google.com/scholar?q=Yuanpei+Chen%2C+Tianhao+Wu%2C+Shengjie+Wang%2C+Xidong+Feng%2C+Jiechuan+Jiang%2C+Zongqing+Lu%2C+Stephen+McAleer%2C+Hao+Dong%2C+Song%2DChun+Zhu%2C+and+Yaodong+Yang%2E+Towards+human%2Dlevel+bimanual+dexterous+manipulation+with+)
13. Hao-Tien Lewis Chiang, Aleksandra Faust, Marek Fiser, and Anthony Francis. Learning navigation behaviors end-to-end with autorl. IEEE Robotics and Automation Letters, 4(2):2007–2014, 2019. [find](https://scholar.google.com/scholar?q=Hao%2DTien+Lewis+Chiang%2C+Aleksandra+Faust%2C+Marek+Fiser%2C+and+Anthony+Francis%2E+Learning+navigation+behaviors+end%2Dto%2Dend+with+autorl%2E+IEEE+Robotics+and+Automation+Letters%2C+4%282%29%3A2007%E2%80%932014%2C+2019%2E)
14. Yan Ding, Xiaohan Zhang, Chris Paxton, and Shiqi Zhang. Task and motion planning with large language models for object rearrangement. arXiv preprint arXiv:2303.06247, 2023. — [arXiv:2303.06247](https://arxiv.org/abs/2303.06247) [find](https://scholar.google.com/scholar?q=Yan+Ding%2C+Xiaohan+Zhang%2C+Chris+Paxton%2C+and+Shiqi+Zhang%2E+Task+and+motion+planning+with+large+language+models+for+object+rearrangement%2E+arXiv+preprint+arXiv%3A2303%2E06247%2C+2023%2E+%E2%80%94+%5BarXiv%3A2303%2E06247%5D%28https%3A)
15. Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju, Jessica Landon, Felix Hill, Nando de Freitas, and Serkan Cabi. Vision-language models as success detectors. arXiv preprint arXiv:2303.07280, 2023a. — [arXiv:2303.07280](https://arxiv.org/abs/2303.07280) [find](https://scholar.google.com/scholar?q=Yuqing+Du%2C+Ksenia+Konyushkova%2C+Misha+Denil%2C+Akhil+Raju%2C+Jessica+Landon%2C+Felix+Hill%2C+Nando+de+Freitas%2C+and+Serkan+Cabi%2E+Vision%2Dlanguage+models+as+success+detectors%2E+arXiv+preprint+arXiv%3A2303%2E07280%2C+202)
16. Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, and Jacob Andreas. Guiding pretraining in reinforcement learning with large language models. arXiv preprint arXiv:2302.06692, 2023b. [find](https://scholar.google.com/scholar?q=Yuqing+Du%2C+Olivia+Watkins%2C+Zihan+Wang%2C+Ce%CC%81dric+Colas%2C+Trevor+Darrell%2C+Pieter+Abbeel%2C+Abhishek+Gupta%2C+and+Jacob+Andreas%2E+Guiding+pretraining+in+reinforcement+learning+with+large+language+models%2E+arXiv+)
17. Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar. [Minedojo: Building open-ended embodied agents with internet-scale knowledge](https://md.research.elamur.ai/free/en/papers/fan-2022-minedojo-building-open-ended-embodied/). Advances in Neural Information Processing Systems, 35: 18343–18362, 2022.
18. Aleksandra Faust, Anthony Francis, and Dar Mehta. Evolving rewards to automate reinforcement learning. arXiv preprint arXiv:1905.07628, 2019. — [arXiv:1905.07628](https://arxiv.org/abs/1905.07628) [find](https://scholar.google.com/scholar?q=Aleksandra+Faust%2C+Anthony+Francis%2C+and+Dar+Mehta%2E+Evolving+rewards+to+automate+reinforcement+learning%2E+arXiv+preprint+arXiv%3A1905%2E07628%2C+2019%2E+%E2%80%94+%5BarXiv%3A1905%2E07628%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1905%2E07628%29)
19. Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In 2017 IEEE international conference on robotics and automation (ICRA), pp. 3389–3396. [find](https://scholar.google.com/scholar?q=Shixiang+Gu%2C+Ethan+Holly%2C+Timothy+Lillicrap%2C+and+Sergey+Levine%2E+Deep+reinforcement+learning+for+robotic+manipulation+with+asynchronous+off%2Dpolicy+updates%2E+In+2017+IEEE+international+conference+on+robo)
20. Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J Russell, and Anca Dragan. Inverse reward design. Advances in neural information processing systems, 30, 2017. [find](https://scholar.google.com/scholar?q=Dylan+Hadfield%2DMenell%2C+Smitha+Milli%2C+Pieter+Abbeel%2C+Stuart+J+Russell%2C+and+Anca+Dragan%2E+Inverse+reward+design%2E+Advances+in+neural+information+processing+systems%2C+30%2C+2017%2E)
21. Ankur Handa, Arthur Allshire, Viktor Makoviychuk, Aleksei Petrenko, Ritvik Singh, Jingzhou Liu, Denys Makoviichuk, Karl Van Wyk, Alexander Zhurkevich, Balakumar Sundaralingam, et al. Dextreme: Transfer of agile in-hand manipulation from simulation to reality. In 2023 IEEE International Conference on… [find](https://scholar.google.com/scholar?q=Ankur+Handa%2C+Arthur+Allshire%2C+Viktor+Makoviychuk%2C+Aleksei+Petrenko%2C+Ritvik+Singh%2C+Jingzhou+Liu%2C+Denys+Makoviichuk%2C+Karl+Van+Wyk%2C+Alexander+Zhurkevich%2C+Balakumar+Sundaralingam%2C+et+al%2E+Dextreme%3A+Transfe)
22. Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger. Deep reinforcement learning that matters. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018. [find](https://scholar.google.com/scholar?q=Peter+Henderson%2C+Riashat+Islam%2C+Philip+Bachman%2C+Joelle+Pineau%2C+Doina+Precup%2C+and+David+Meger%2E+Deep+reinforcement+learning+that+matters%2E+In+Proceedings+of+the+AAAI+conference+on+artificial+intelligence)
23. Jonathan Ho and Stefano Ermon. Generative adversarial imitation learning. Advances in neural information processing systems, 29, 2016. [find](https://scholar.google.com/scholar?q=Jonathan+Ho+and+Stefano+Ermon%2E+Generative+adversarial+imitation+learning%2E+Advances+in+neural+information+processing+systems%2C+29%2C+2016%2E)
24. Taylor Howell, Nimrod Gileadi, Saran Tunyasuvunakool, Kevin Zakka, Tom Erez, and Yuval Tassa. Predictive sampling: Real-time behaviour synthesis with mujoco. arXiv preprint arXiv:2212.00541, 2022. — [arXiv:2212.00541](https://arxiv.org/abs/2212.00541) [find](https://scholar.google.com/scholar?q=Taylor+Howell%2C+Nimrod+Gileadi%2C+Saran+Tunyasuvunakool%2C+Kevin+Zakka%2C+Tom+Erez%2C+and+Yuval+Tassa%2E+Predictive+sampling%3A+Real%2Dtime+behaviour+synthesis+with+mujoco%2E+arXiv+preprint+arXiv%3A2212%2E00541%2C+2022%2E+%E2%80%94+%5B)
25. Siyuan Huang, Zhengkai Jiang, Hao Dong, Yu Qiao, Peng Gao, and Hongsheng Li. Instruct2act: Mapping multi-modality instructions to robotic actions with large language model. arXiv preprint arXiv:2305.11176, 2023. — [arXiv:2305.11176](https://arxiv.org/abs/2305.11176) [find](https://scholar.google.com/scholar?q=Siyuan+Huang%2C+Zhengkai+Jiang%2C+Hao+Dong%2C+Yu+Qiao%2C+Peng+Gao%2C+and+Hongsheng+Li%2E+Instruct2act%3A+Mapping+multi%2Dmodality+instructions+to+robotic+actions+with+large+language+model%2E+arXiv+preprint+arXiv%3A2305%2E1)
26. Siddharth Karamcheti, Suraj Nair, Annie S Chen, Thomas Kollar, Chelsea Finn, Dorsa Sadigh, and Percy Liang. Language-driven representation learning for robotics. arXiv preprint arXiv:2302.12766, 2023. [find](https://scholar.google.com/scholar?q=Siddharth+Karamcheti%2C+Suraj+Nair%2C+Annie+S+Chen%2C+Thomas+Kollar%2C+Chelsea+Finn%2C+Dorsa+Sadigh%2C+and+Percy+Liang%2E+Language%2Ddriven+representation+learning+for+robotics%2E+arXiv+preprint+arXiv%3A2302%2E12766%2C+2023%2E)
27. W Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, and Peter Stone. Reward (mis) design for autonomous driving. Artificial Intelligence, 316:103829, 2023. [find](https://scholar.google.com/scholar?q=W+Bradley+Knox%2C+Alessandro+Allievi%2C+Holger+Banzhaf%2C+Felix+Schmitt%2C+and+Peter+Stone%2E+Reward+%28mis%29+design+for+autonomous+driving%2E+Artificial+Intelligence%2C+316%3A103829%2C+2023%2E)
28. Ashish Kumar, Zipeng Fu, Deepak Pathak, and Jitendra Malik. Rma: Rapid motor adaptation for legged robots. arXiv preprint arXiv:2107.04034, 2021. [find](https://scholar.google.com/scholar?q=Ashish+Kumar%2C+Zipeng+Fu%2C+Deepak+Pathak%2C+and+Jitendra+Malik%2E+Rma%3A+Rapid+motor+adaptation+for+legged+robots%2E+arXiv+preprint+arXiv%3A2107%2E04034%2C+2021%2E)
29. Minae Kwon, Sang Michael Xie, Kalesha Bullard, and Dorsa Sadigh. [Reward design with language models](https://md.research.elamur.ai/free/en/papers/kwon-2023-reward-design-language-models/). arXiv preprint arXiv:2303.00001, 2023.
30. Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng. Code as policies: Language model programs for embodied control. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 9493–9500. [find](https://scholar.google.com/scholar?q=Jacky+Liang%2C+Wenlong+Huang%2C+Fei+Xia%2C+Peng+Xu%2C+Karol+Hausman%2C+Brian+Ichter%2C+Pete+Florence%2C+and+Andy+Zeng%2E+Code+as+policies%3A+Language+model+programs+for+embodied+control%2E+In+2023+IEEE+International+Conf)
31. Kevin Lin, Christopher Agia, Toki Migimatsu, Marco Pavone, and Jeannette Bohg. Text2motion: From natural language instructions to feasible plans. arXiv preprint arXiv:2303.12153, 2023. — [arXiv:2303.12153](https://arxiv.org/abs/2303.12153) [find](https://scholar.google.com/scholar?q=Kevin+Lin%2C+Christopher+Agia%2C+Toki+Migimatsu%2C+Marco+Pavone%2C+and+Jeannette+Bohg%2E+Text2motion%3A+From+natural+language+instructions+to+feasible+plans%2E+arXiv+preprint+arXiv%3A2303%2E12153%2C+2023%2E+%E2%80%94+%5BarXiv%3A2303%2E1)
32. Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. Llm+ p: Empowering large language models with optimal planning proficiency. arXiv preprint arXiv:2304.[11477, 2023a](https://md.research.elamur.ai/free/en/papers/liu-2023-llm-p-empowering-large-language/).
33. Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. [Visual instruction tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-visual-instruction-tuning/). arXiv preprint arXiv:2304.08485, 2023b.
34. Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang. Vip: Towards universal visual reward and representation via value-implicit pre-training. arXiv preprint arXiv:2210.00030, 2022. — [arXiv:2210.00030](https://arxiv.org/abs/2210.00030) [find](https://scholar.google.com/scholar?q=Yecheng+Jason+Ma%2C+Shagun+Sodhani%2C+Dinesh+Jayaraman%2C+Osbert+Bastani%2C+Vikash+Kumar%2C+and+Amy+Zhang%2E+Vip%3A+Towards+universal+visual+reward+and+representation+via+value%2Dimplicit+pre%2Dtraining%2E+arXiv+preprint)
35. Yecheng Jason Ma, William Liang, Vaidehi Som, Vikash Kumar, Amy Zhang, Osbert Bastani, and Dinesh Jayaraman. Liv: Language-image representations and rewards for robotic control. arXiv preprint arXiv:2306.00958, 2023. — [arXiv:2306.00958](https://arxiv.org/abs/2306.00958) [find](https://scholar.google.com/scholar?q=Yecheng+Jason+Ma%2C+William+Liang%2C+Vaidehi+Som%2C+Vikash+Kumar%2C+Amy+Zhang%2C+Osbert+Bastani%2C+and+Dinesh+Jayaraman%2E+Liv%3A+Language%2Dimage+representations+and+rewards+for+robotic+control%2E+arXiv+preprint+arXiv%3A2)
36. Denys Makoviichuk and Viktor Makoviychuk. rl-games: A high-performance framework for reinforcement learning. [https://github.com/Denys88/rl_games](https://github.com/Denys88/rl_games), May 2021. [↗](https://github.com/Denys88/rl_games)
37. Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et al. [Isaac gym: High performance gpu-based physics simulation for robot learning](https://md.research.elamur.ai/free/en/papers/makoviychuk-2021-isaac-gym-high-performance-gpu/). arXiv preprint arXiv:2108.10470, 2021.
38. Gabriel B Margolis, Ge Yang, Kartik Paigwar, Tao Chen, and Pulkit Agrawal. Rapid locomotion via reinforcement learning. arXiv preprint arXiv:2205.02824, 2022. — [arXiv:2205.02824](https://arxiv.org/abs/2205.02824) [find](https://scholar.google.com/scholar?q=Gabriel+B+Margolis%2C+Ge+Yang%2C+Kartik+Paigwar%2C+Tao+Chen%2C+and+Pulkit+Agrawal%2E+Rapid+locomotion+via+reinforcement+learning%2E+arXiv+preprint+arXiv%3A2205%2E02824%2C+2022%2E+%E2%80%94+%5BarXiv%3A2205%2E02824%5D%28https%3A%2F%2Farxiv%2Eorg%2Fab)
39. Scott Niekum, Andrew G Barto, and Lee Spector. Genetic programming for reward function search. IEEE Transactions on Autonomous Mental Development, 2(2):83–90, 2010. [find](https://scholar.google.com/scholar?q=Scott+Niekum%2C+Andrew+G+Barto%2C+and+Lee+Spector%2E+Genetic+programming+for+reward+function+search%2E+IEEE+Transactions+on+Autonomous+Mental+Development%2C+2%282%29%3A83%E2%80%9390%2C+2010%2E)
40. OpenAI. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/), 2023.
41. Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al. [Code llama: Open foundation models for code](https://md.research.elamur.ai/free/en/papers/roziere-2024-code-llama-open-foundation-models/). arXiv preprint arXiv:2308.12950, 2023.
42. Stuart J Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, USA, 1st edition, 1995. [find](https://scholar.google.com/scholar?q=Stuart+J+Russell+and+Peter+Norvig%2E+Artificial+Intelligence%3A+A+Modern+Approach%2E+Prentice+Hall%2C+Englewood+Cliffs%2C+NJ%2C+USA%2C+1st+edition%2C+1995%2E)
43. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. [find](https://scholar.google.com/scholar?q=John+Schulman%2C+Filip+Wolski%2C+Prafulla+Dhariwal%2C+Alec+Radford%2C+and+Oleg+Klimov%2E+Proximal+policy+optimization+algorithms%2E+arXiv+preprint+arXiv%3A1707%2E06347%2C+2017%2E)
44. Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.[11366, 2023](https://md.research.elamur.ai/free/en/papers/shinn-2023-reflexion-language-agents-verbal-reinforcement/).
45. Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B Tenenbaum, Leslie Pack Kaelbling, and Michael Katz. Generalized planning in pddl domains with pretrained large language models. arXiv preprint arXiv:2305.11014, 2023. [find](https://scholar.google.com/scholar?q=Tom+Silver%2C+Soham+Dan%2C+Kavitha+Srinivas%2C+Joshua+B+Tenenbaum%2C+Leslie+Pack+Kaelbling%2C+and+Michael+Katz%2E+Generalized+planning+in+pddl+domains+with+pretrained+large+language+models%2E+arXiv+preprint+arXiv%3A2)
46. Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. [Progprompt: Generating situated robot task plans using large language models](https://md.research.elamur.ai/free/en/papers/singh-2022-progprompt-generating-situated-robot-task/). In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 11523–115…
47. Satinder Singh, Richard L. Lewis, , and Andrew G. Barto. Where do rewards come from? In Proceedings of the International Symposium on AI Inspired Biology - A Symposium at the AISB 2010 Convention, pp. 111–116, 2010. ISBN 1902956923. [find](https://scholar.google.com/scholar?q=Satinder+Singh%2C+Richard+L%2E+Lewis%2C+%2C+and+Andrew+G%2E+Barto%2E+Where+do+rewards+come+from%3F+In+Proceedings+of+the+International+Symposium+on+AI+Inspired+Biology+%2D+A+Symposium+at+the+AISB+2010+Convention%2C+pp%2E)
48. Laura Smith, Ilya Kostrikov, and Sergey Levine. A walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning. arXiv preprint arXiv:2208.07860, 2022. — [arXiv:2208.07860](https://arxiv.org/abs/2208.07860) [find](https://scholar.google.com/scholar?q=Laura+Smith%2C+Ilya+Kostrikov%2C+and+Sergey+Levine%2E+A+walk+in+the+park%3A+Learning+to+walk+in+20+minutes+with+model%2Dfree+reinforcement+learning%2E+arXiv+preprint+arXiv%3A2208%2E07860%2C+2022%2E+%E2%80%94+%5BarXiv%3A2208%2E07860%5D%28h)
49. Richard S Sutton and Andrew G Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, USA, 2nd edition, 2018. [find](https://scholar.google.com/scholar?q=Richard+S+Sutton+and+Andrew+G+Barto%2E+Reinforcement+Learning%3A+An+Introduction%2E+MIT+Press%2C+Cambridge%2C+MA%2C+USA%2C+2nd+edition%2C+2018%2E)
50. Emanuel Todorov, Tom Erez, and Yuval Tassa. Mujoco: A physics engine for model-based control. In 2012 IEEE/RSJ international conference on intelligent robots and systems, pp. 5026–5033. [find](https://scholar.google.com/scholar?q=Emanuel+Todorov%2C+Tom+Erez%2C+and+Yuval+Tassa%2E+Mujoco%3A+A+physics+engine+for+model%2Dbased+control%2E+In+2012+IEEE%2FRSJ+international+conference+on+intelligent+robots+and+systems%2C+pp%2E+5026%E2%80%935033%2E)
51. Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023a. [find](https://scholar.google.com/scholar?q=Guanzhi+Wang%2C+Yuqi+Xie%2C+Yunfan+Jiang%2C+Ajay+Mandlekar%2C+Chaowei+Xiao%2C+Yuke+Zhu%2C+Linxi+Fan%2C+and+Anima+Anandkumar%2E+Voyager%3A+An+open%2Dended+embodied+agent+with+large+language+models%2E+arXiv+preprint+arXiv%3A23)
52. Huaxiaoyue Wang, Gonzalo Gonzalez-Pumariega, Yash Sharma, and Sanjiban Choudhury. Demo2code: From summarizing demonstrations to synthesizing code via extended chain-of-thought. arXiv preprint arXiv:2305.16744, 2023b. — [arXiv:2305.16744](https://arxiv.org/abs/2305.16744) [find](https://scholar.google.com/scholar?q=Huaxiaoyue+Wang%2C+Gonzalo+Gonzalez%2DPumariega%2C+Yash+Sharma%2C+and+Sanjiban+Choudhury%2E+Demo2code%3A+From+summarizing+demonstrations+to+synthesizing+code+via+extended+chain%2Dof%2Dthought%2E+arXiv+preprint+arXiv%3A23)
53. Grady Williams, Andrew Aldrich, and Evangelos Theodorou. Model predictive path integral control using covariance variable importance sampling. arXiv preprint arXiv:1509.01149, 2015. [find](https://scholar.google.com/scholar?q=Grady+Williams%2C+Andrew+Aldrich%2C+and+Evangelos+Theodorou%2E+Model+predictive+path+integral+control+using+covariance+variable+importance+sampling%2E+arXiv+preprint+arXiv%3A1509%2E01149%2C+2015%2E)
54. Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh. Translating natural language to planning goals with large-language models. arXiv preprint arXiv:2302.05128, 2023. — [arXiv:2302.05128](https://arxiv.org/abs/2302.05128) [find](https://scholar.google.com/scholar?q=Yaqi+Xie%2C+Chen+Yu%2C+Tongyao+Zhu%2C+Jinbin+Bai%2C+Ze+Gong%2C+and+Harold+Soh%2E+Translating+natural+language+to+planning+goals+with+large%2Dlanguage+models%2E+arXiv+preprint+arXiv%3A2302%2E05128%2C+2023%2E+%E2%80%94+%5BarXiv%3A2302%2E051)
55. Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. The dawn of lmms: Preliminary explorations with gpt-4v (ision). arXiv preprint arXiv:2309.[17421, 9, 2023](https://md.research.elamur.ai/free/en/papers/yang-2023-dawn-lmms-preliminary-explorations-gpt/).
56. Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, et al. Language to rewards for robotic skill synthesis. arXiv preprint arXiv:2306.08647, 2023. — [arXiv:2306.08647](https://arxiv.org/abs/2306.08647) [find](https://scholar.google.com/scholar?q=Wenhao+Yu%2C+Nimrod+Gileadi%2C+Chuyuan+Fu%2C+Sean+Kirmani%2C+Kuang%2DHuei+Lee%2C+Montse+Gonzalez+Arenas%2C+Hao%2DTien+Lewis+Chiang%2C+Tom+Erez%2C+Leonard+Hasenclever%2C+Jan+Humplik%2C+et+al%2E+Language+to+rewards+for+robotic+s)
57. Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. [Minigpt-4: Enhancing vision-language understanding with advanced large language models](https://md.research.elamur.ai/free/en/papers/zhu-2023-minigpt-4-enhancing-vision-language/). arXiv preprint arXiv:2304.10592, 2023.
58. Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, Anind K Dey, et al. Maximum entropy inverse reinforcement learning. In Aaai, volume 8, pp. 1433–1438. Chicago, IL, USA, 2008. [find](https://scholar.google.com/scholar?q=Brian+D+Ziebart%2C+Andrew+L+Maas%2C+J+Andrew+Bagnell%2C+Anind+K+Dey%2C+et+al%2E+Maximum+entropy+inverse+reinforcement+learning%2E+In+Aaai%2C+volume+8%2C+pp%2E+1433%E2%80%931438%2E+Chicago%2C+IL%2C+USA%2C+2008%2E)

*Updated 2 November 2026*