Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wentorai/research-plugins --skill reinforcement-learning-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wentorai/research-plugins/reinforcement-learning-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/reinforcement-learning-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/reinforcement-learning-guide/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wentorai/research-plugins/reinforcement-learning-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/reinforcement-learning-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00015 | $0.02355 |
| Opus 5 | $0.00008 | $0.01177 |
| Sonnet 5 | $0.00003 | $0.00471 |
| Haiku 4.5 | $0.00002 | $0.00235 |
Grade A, and why
reinforcement-learning-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reinforcement Learning Guide
Understand and implement reinforcement learning algorithms from tabular methods through deep RL, including policy gradients, actor-critic, and model-based approaches.
RL Fundamentals
The RL Framework
An agent interacts with an environment to maximize cumulative reward:
Agent Environment
| |
|--- action a_t ---------->|
| |--- next state s_{t+1}
|<-- reward r_t, state s_t |--- reward r_{t+1}
| |
| Concept | Symbol | Definition |
|---|---|---|
| State | s | Observation of the environment |
| Action | a | Decision made by the agent |
| Reward | r | Scalar feedback signal |
| Policy | pi(a|s) | Mapping from states to actions |
| Value function | V(s) | Expected cumulative reward from state s |
| Q-function | Q(s, a) | Expected cumulative reward from (s, a) |
| Discount factor | gamma | Weight of future vs. immediate rewards (0-1) |
| Return | G_t | Sum of discounted future rewards from time t |
Key Equations
# Return (discounted cumulative reward)
G_t = r_t + gamma * r_{t+1} + gamma^2 * r_{t+2} + ...
# Bellman equation for V
V(s) = E[r + gamma * V(s') | s]
# Bellman equation for Q
Q(s, a) = E[r + gamma * max_a' Q(s', a') | s, a]
# Policy gradient theorem
gradient J(theta) = E[gradient log pi_theta(a|s) * Q(s, a)]
Algorithm Taxonomy
| Category | Algorithm | Key Idea | On/Off Policy |
|---|---|---|---|
| Value-based | Q-Learning | Learn Q(s,a), act greedily | Off-policy |
| DQN | Q-Learning + neural net + replay buffer | Off-policy | |
| Double DQN | Two networks to reduce overestimation | Off-policy | |
| Dueling DQN | Separate value and advantage streams | Off-policy | |
| Policy gradient | REINFORCE | Monte Carlo policy gradient | On-policy |
| PPO | Clipped surrogate objective | On-policy | |
| TRPO | Trust region constraint | On-policy | |
| Actor-Critic | A2C/A3C | Advantage actor-critic (parallel) | On-policy |
| SAC | Maximum entropy + off-policy AC | Off-policy | |
| TD3 | Twin delayed DDPG | Off-policy | |
| Model-based | Dreamer | World model + imagination | On-policy |
| MBPO | Model-based policy optimization | Off-policy | |
| MuZero | Learned model + planning (MCTS) | Off-policy |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 255 lines · 15 tokens per session scan A d3c30f8ad0b3
reinforcement-learning-guide is a skill published in the GitHub repository wentorai/research-plugins (291 stars, last pushed 2mo ago), licensed MIT. It adds 15 tokens to every session and 2,355 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
fs-notebook-tabs
A computer-science capstone: an on-device ML keyboard that predicts next words privately — problem, method, evaluation, and defense answers. Built as a decision-grade coursework defense deck for professor, defense committee.
implementing-llms-litgpt
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
rwkv-architecture
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
nanogpt
Educational GPT implementation in 300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).
kaggle-learner
This skill should be used when the user asks to "learn from Kaggle", "study Kaggle solutions", "analyze Kaggle competitions", or mentions Kaggle competition URLs. Provides access to extracted knowledge from winning Kaggle solutions across NLP, CV, time series, tabular, and multimodal domains.
data-science-engineering-foundation
State, resume, reconstruction, job-lifecycle, transition, and flow-state mechanics for the Data Science and Engineering Coach. Loaded by the coach; not a user entry point.