Spinning Up Deep Rl

Spinning Up in Deep RL

Author: Joshua Achiam (OpenAI) | Source: spinningup.readthedocs.io, MIT | Chapters: 20 | Generated: 2026-08-25

How to Use This Skill

• No argument — load the core frameworks below
• A topic — ask about advantage function, target networks, entropy regularization; I resolve it through the Topic Index and read that chapter file
• chNN — I load that chapter's summary
• "what chapters do you have?" — the full index

/cs:spinning-up-deep-rl                      # core frameworks + chapter index
/cs:spinning-up-deep-rl entropy regularization   # topic index -> ch19, read that chapter
/cs:spinning-up-deep-rl ch09                 # one chapter summary

When you ask about something not in Core Frameworks, I read the relevant chapter file before answering rather than guessing from the index.

Core Frameworks & Mental Models

The RL problem

pi* = argmax_pi J(pi), where J(pi) = E_{tau~pi}[R(tau)]. Every algorithm approximates this; where it substitutes a different objective (a Bellman residual, a surrogate), that substitution is the source of its failure modes. Four value functions — V^pi, Q^pi, V*, Q* — all obey Bellman self-consistency, and a*(s) = argmax_a Q*(s,a) is why Q-learning is a viable family at all. Advantage A^pi(s,a) = Q^pi(s,a) - V^pi(s) is the relative-quality signal policy gradients run on. (ch07)

The two branching questions

Place any algorithm by asking: does it have or learn a model, and what does it learn (policy, Q-function, value function, model). That generates the whole landscape. (ch08)

Policy optimization vs Q-learning — the central trade-off

• Policy optimization is principled: you directly optimize the thing you want. Stable and reliable. On-policy, so it cannot reuse data, so it is sample-hungry.
• Q-learning only indirectly optimizes performance, by training Q_theta to satisfy a self-consistency equation. Many failure modes, so less stable. But substantially more sample efficient when it works, because it reuses everything.
• Satisfying the Bellman equations well carries no guarantee of good policy performance.
• The two are not exclusive — DDPG and SAC live between them deliberately. (ch08)

The policy gradient template

grad J = E[ sum_t grad log pi_theta(a_t|s_t) * Phi_t ]. Five valid choices of Phi_t: full return, reward-to-go, reward-to-go minus a baseline, Q^pi, and A^pi. All share an expectation and differ in variance. Two rules get you from the first to the last:

• Don't let the past distract you — drop rewards obtained before the action. Those terms had zero mean and nonzero variance: pure noise.
• Baselines — by the EGLP lemma, any state-only b(s) can be added or subtracted freely. The standard choice is V^pi(s_t), learned by MSE regression onto reward-to-go. (ch09)

The policy-gradient loss is not a loss function

Its data distribution depends on the parameters, and it does not measure performance even in expectation. Only at the current parameters, with data from those parameters, does it have the negative gradient of performance. You can send it to negative infinity while performance craters, and it usually will. Only average return means anything. (ch09)

Broken RL code almost always fails silently

It runs fine; the agent just never learns. Usually something is computed with the wrong equation, on the wrong distribution, or piped to the wrong place. If it doesn't work, assume there's a bug before touching hyperparameters. Debug by measuring everything and reading the code critically. The archetype is one missing squeeze: a [N] vs [N,1] shape mismatch is broadcast-compatible, raises nothing, and silently turns the Bellman backup into an [N,N] matrix. (ch10, ch12)

Learn by doing

Write your own implementations, shortest correct version of each, simplest algorithms first. VPG, DQN, A2C, PPO, DDPG, roughly in that order; ~250-300 lines each. Single-threaded before parallel. Iterate fast in simple environments — under 5 minutes turnaround at the debug stage. Do not attempt Atari or Humanoid before the toy task works. Read papers for their ablations and supplementary material, but do not overfit to paper details (the original DDPG's architecture, init scheme and batch norm are not strictly necessary) or to existing implementations (their abstractions serve reuse, not your single use case). (ch10)

Rigor: four standards

1. Fair comparisons — tune the baseline as hard as your method; never handicap it.
2. Remove stochasticity as a confounder — at least 3 seeds, 10 or more to be thorough. Two seed groups can produce curves that look like different distributions.
3. High-integrity experiments — launch fresh final runs and precommit to reporting them. Tuning produces hypotheses; final runs produce conclusions.
4. Check each claim separately — ablate every design decision. (ch10)

The safe-step family (on-policy)

VPG takes an unconstrained gradient step, so a single bad step can collapse performance. TRPO constrains the step in KL-divergence between policies, not distance in parameter space, then backtracking-line-searches until the exact constraint holds. PPO drops the constraint and instead clips the objective so the policy gains nothing by moving far, which is first-order, far simpler, and empirically at least as good. (ch14, ch15, ch16)

The overestimation family (off-policy)

DDPG amortizes the intractable continuous max_a Q(s,a) into a learned policy: max_a Q(s,a) ~= Q(s, mu(s)). It needs a replay buffer (licensed because the Bellman equation is indifferent to how data was collected) and target networks (because the target otherwise depends on the parameters being trained). Its failure mode is Q-value overestimation, which the policy actively exploits. TD3 answers with clipped double-Q, delayed policy updates and target policy smoothing. SAC adds entropy regularization, making the explore-exploit trade-off an explicit coefficient alpha. (ch17, ch18, ch19)

Chapter Index

Topic Index

• Advantage function ch07, ch09, ch14
• Baselines ch09
• Bellman equations ch07, ch17
• Benchmarks / parity ch13, ch01
• Clipped double-Q ch18, ch19
• Continuous action spaces ch07, ch17
• Debugging / silent failure ch10, ch12
• DDPG ch17, ch03, ch08
• Entropy regularization ch19
• Exploration vs exploitation ch14, ch17, ch19
• GAE ch09, ch14
• Installation ch02
• KL divergence / trust region ch15, ch16
• Logging ch20, ch05
• MDPs ch07
• Model-based RL ch08
• MPI / parallelization ch20, ch02, ch04
• MSBE ch17
• Off-policy ch03, ch08, ch17
• On-policy ch03, ch08, ch14
• Papers / literature ch11, ch10
• Plotting ch06, ch13
• Policies (categorical, Gaussian, squashed) ch07, ch19
• Policy gradient derivation ch09
• PPO ch16, ch03
• Q-learning ch08, ch07
• Replay buffer ch17
• Reparameterization trick ch19, ch10
• Research process / rigor ch10, ch13
• Reward-to-go ch09
• Running experiments ch04, ch05
• SAC ch19, ch03, ch08
• Seeds / variance ch10, ch13, ch04
• Target networks / polyak ch17, ch18
• TD3 ch18, ch12
• TRPO ch15, ch03
• Value functions ch07, ch09
• VPG ch14, ch09

Supporting Files

• glossary.md · patterns.md · cheatsheet.md

Scope & Limits

Covers the Spinning Up documentation only, as of the January 2020 PyTorch update. It does not cover: DQN and the discrete-action value-learning family (referenced, never implemented here), recurrent or convolutional architectures, partially-observed settings, model-based implementations, exploration/meta-RL/hierarchy beyond ch11's reading list, or any deep RL work after early 2020. The six implementations are educational; ch13 says which are research-grade. For topics beyond this source, I say so rather than improvising.

Compiled from OpenAI's Spinning Up in Deep RL documentation (MIT, Copyright (c) 2018 OpenAI), primarily developed by Joshua Achiam. Structured study notes, not a reproduction of the source.