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All were excerpt caches. Tuning playbook 662 -> 16216 words, ML Yearning 527 -> 26084 (the whole book, was ch13-19), bekman 579 -> 2357 (three concatenated pages), Spinning Up 842 -> 3353. The Qwen3 report was truncated mid-section 4.3 and now runs to the appendix.
46 lines
3.4 KiB
Markdown
46 lines
3.4 KiB
Markdown
# Spinning Up as a Deep RL Researcher - source graph and research-taste excerpts
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Primary source: https://spinningup.openai.com/en/latest/spinningup/spinningup.html
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Author: Joshua Achiam, OpenAI
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Date: October 13th, 2018
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Related local cache: docs/evidence/spinningup_researcher.md
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Fetch-status: index only. The full page text now lives in docs/evidence/spinningup_researcher.md (fetched 2026-08-15); this file keeps the source graph so the two caches do not hold the same 3.3k words twice (CLAUDE agent)
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Use: RL research-process evidence, especially for source graph, fair comparisons, seeds, preregistration, and ablations.
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## Why this matters for agents
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Spinning Up is not just an RL textbook page. Its researcher page is a compact research apprenticeship guide. It points to the same battle-tested debugging and reproducibility references already cached in this repo, then adds project selection and rigorous comparison advice.
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## Quotes
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The quotes that used to sit here (simplicity is critical, iterate fast in simple
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environments, avoid reinventing the wheel, handicap the baseline, seeds,
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preregistration, ablations) are all in the full page text at
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[spinningup_researcher.md](spinningup_researcher.md). Read that file for the
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wording; this one only carries the source graph. (CLAUDE agent, 2026-08-15)
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## Source graph
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Spinning Up intro references, with local status:
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- Alex Irpan, Deep Reinforcement Learning Doesn't Work Yet: https://www.alexirpan.com/2018/02/14/rl-hard.html. Local cache: docs/evidence/alexirpan_rl_hard.md.
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- Islam et al., Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control: https://arxiv.org/abs/1708.04133. Not separately cached; discussed/cited inside Henderson local cache.
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- Henderson et al., Deep Reinforcement Learning that Matters: https://arxiv.org/abs/1709.06560. Local cache: docs/evidence/henderson_2018_deep_rl_matters.md.
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- Matthew Rahtz, Lessons Learned Reproducing a Deep RL Paper: http://amid.fish/reproducing-deep-rl. Local cache: docs/evidence/amid_fish_reproducing_deep_rl.md.
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- David Silver UCL RL course: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html. Not cached.
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- Berkeley Deep RL course: http://rll.berkeley.edu/deeprlcourse/. Not cached.
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- Deep RL Bootcamp lectures: https://sites.google.com/view/deep-rl-bootcamp/lectures. Reddit index cache: docs/evidence/reddit_deeprl_bootcamp_2017_75m5vd.md.
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- John Schulman, Nuts and Bolts of Deep RL: http://joschu.net/docs/nuts-and-bolts.pdf. Local cache: docs/evidence/joschu_nuts_and_bolts.md.
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- Tim Rocktaschel et al., Advice for Short-term Machine Learning Research Projects: https://rockt.github.io/2018/08/29/msc-advice.html. Not cached.
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- Catherine Olsson / 80,000 Hours, ML Engineering for AI Safety & Robustness: https://80000hours.org/articles/ml-engineering-career-transition-guide/. Not cached.
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## Likely follow-up cache candidates
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Priority 1:
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- Chris Olah, research taste: short and directly named by Nanda.
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- Jacob Steinhardt, Research as a Stochastic Decision Process: directly named by Nanda for prioritization.
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- Tim Rocktaschel et al., short-term ML research projects: directly named by Spinning Up for research growth.
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Priority 2:
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- David Silver/UCL, Berkeley Deep RL, Deep RL Bootcamp: curriculum material, less directly research-taste except via RL mastery.
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- Catherine Olsson/80k: career/field-entry framing; useful if the skill expands beyond project-level research taste.
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