References: Challenges and open problems

Primary source

• Levine, S. (2023). Berkeley CS285, Deep Reinforcement Learning, lecture on Challenges and open problems. http://rail.eecs.berkeley.edu/deeprlcourse/. Lecture video at https://www.youtube.com/playlist?list=PL_iWQOsE6TfVYGEGiAOMaOzzv41Jfm_Ps.

Sample efficiency

• Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. (2023). Mastering Diverse Domains through World Models. arXiv:2301.04104. https://arxiv.org/abs/2301.04104 DreamerV3; world-model architecture achieving competitive performance across many environments with strong sample efficiency.
• Ye, W., Liu, S., Kurutach, T., Abbeel, P., & Gao, Y. (2021). Mastering Atari Games with Limited Data. NeurIPS 2021. https://arxiv.org/abs/2111.00210 EfficientZero; data-efficient MuZero variant.
• Wu, P., Escontrela, A., Hafner, D., Goldberg, K., & Abbeel, P. (2022). DayDreamer: World Models for Physical Robot Learning. CoRL 2022. https://arxiv.org/abs/2206.14176 World-models for real-robot learning with reduced sample requirements.

Safety and alignment

• Hendrycks, D., Mazeika, M., & Woodside, T. (2023). An Overview of Catastrophic AI Risks. arXiv:2306.12001. https://arxiv.org/abs/2306.12001 Comprehensive overview of AI safety risk categories.
• Ngo, R., Chan, L., & Mindermann, S. (2022). The Alignment Problem from a Deep Learning Perspective. arXiv:2209.00626. https://arxiv.org/abs/2209.00626 Deep-learning-specific framing of the alignment problem.
• Bai, Y., Kadavath, S., Kundu, S., et al. (2022). Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073. https://arxiv.org/abs/2212.08073 Constitutional AI as a practical scalable-oversight method.
• Irving, G., Christiano, P., & Amodei, D. (2018). AI Safety via Debate. arXiv:1805.00899. https://arxiv.org/abs/1805.00899 The debate proposal for scalable oversight.
• Christiano, P., Shlegeris, B., & Amodei, D. (2018). Supervising Strong Learners by Amplifying Weak Experts. arXiv:1810.08575. https://arxiv.org/abs/1810.08575 Iterated amplification.
• Olah, C., Mordvintsev, A., & Schubert, L. (2017-2020). The Distill Circuits Thread. https://distill.pub/2020/circuits/ Mechanistic interpretability foundations.
• Templeton, A., Conerly, T., Marcus, J., et al. (2024). Scaling Monosemanticity. Anthropic Transformer Circuits. https://transformer-circuits.pub/2024/scaling-monosemanticity/ Sparse autoencoders at production scale.

Generalization

• Schölkopf, B., Locatello, F., Bauer, S., et al. (2021). Towards Causal Representation Learning. Proceedings of the IEEE, 109(5), 612-634. https://arxiv.org/abs/2102.11107 The causal-representation research direction.
• Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A. A., & Hardt, M. (2020). Test-Time Training with Self-Supervision for Generalization Under Distribution Shifts. ICML 2020. https://arxiv.org/abs/1909.13231 Test-time adaptation for generalization.
• Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017). Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World. IROS 2017. https://arxiv.org/abs/1703.06907 Domain randomization for sim-to-real generalization.
• Cobbe, K., Hesse, C., Hilton, J., & Schulman, J. (2020). Leveraging Procedural Generation to Benchmark Reinforcement Learning. ICML 2020. https://arxiv.org/abs/1912.01588 Procgen benchmark for generalization in deep RL.

Real-world deployment

• Andrychowicz, M., Baker, B., Chociej, M., et al. (2020). Learning Dexterous In-Hand Manipulation. IJRR, 39(1), 3-20. https://arxiv.org/abs/1808.00177 Sim-to-real for dexterous manipulation with domain randomization.
• Kalashnikov, D., Irpan, A., Pastor, P., et al. (2018). QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. CoRL 2018. https://arxiv.org/abs/1806.10293 Production-scale real-robot RL pipeline.
• Akkaya, I., Andrychowicz, M., Chociej, M., et al. (2019). Solving Rubik’s Cube with a Robot Hand. arXiv:1910.07113. https://arxiv.org/abs/1910.07113 Automated domain randomization for sim-to-real on a challenging task.
• Smith, L., Kostrikov, I., & Levine, S. (2022). A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning. arXiv:2208.07860. https://arxiv.org/abs/2208.07860 Real-robot online RL with rapid training.

Surveys covering multiple frontiers

• Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018). Deep Reinforcement Learning that Matters. AAAI 2018. https://arxiv.org/abs/1709.06560 Famous paper on reproducibility and methodology in deep RL; relevant to evaluating frontier claims.
• Kirk, R., Zhang, A., Grefenstette, E., & Rocktäschel, T. (2023). A Survey of Zero-shot Generalisation in Deep Reinforcement Learning. JAIR, 76, 201-264. https://arxiv.org/abs/2111.09794 Survey covering generalization in deep RL.
• Amodei, D., Olah, C., Steinhardt, J., et al. (2016). Concrete Problems in AI Safety. arXiv:1606.06565. https://arxiv.org/abs/1606.06565 The original concrete AI safety problems paper; many are still open.

Note on the source mix and Track 18 close

This is the final lesson of Track 18 (Deep Reinforcement Learning). The references span all four open frontiers identified in the lesson body, with each frontier represented by 3-5 active research papers and the relevant surveys. The CS285 primary source covers the field’s open-problems framing as presented by Levine in 2023; the algorithmic and applied references are the canonical primary sources for each sub-area.

Across Track 18 (18 lessons total) the references span CS285 lectures 1-19 (Levine 2023) as the algorithmic-foundations primary source, 50+ primary papers across the algorithmic and applied sub-fields, standard benchmarks (Atari, MuJoCo, D4RL, Meta-World, Procgen), and recent surveys. The track is drafted under the cold-track full-original discipline with the math-gloss convention applied throughout for audio-narration readiness.

Track 18 closes.

Source material

Source curriculum (structural mirror, cited as further study):
• UC Berkeley CS285: Deep Reinforcement Learning (Sergey Levine)
  Course page: http://rail.eecs.berkeley.edu/deeprlcourse/
  Lecture videos: YouTube (link-out only)
Clawdemy's lessons are original prose that follows the pedagogical arc of this
source. We do not reproduce or transcribe it; we cite it as a recommended
companion. All rights to the original material remain with its authors.