OER·harvester

Competency catalogue

The 104 active T2.3 competencies: the vocabulary the harvester searches with, the descriptor the alignment reads, and what the alignment found. Compose a harvest →

What the alignment foundHow often each was searched

How many resources the alignment reads as contributing to each competency or developing it: decided at level 2 or 3, or not decided yet and more likely than not to reach 2. 54 competencies have none, 24 one or two. Every figure is a lower bound: on a sample graded on every competency, the map counts 33% of what was graded 2 or 3, and almost nothing of the 5 competencies underlined with dots.

How many times a harvest has searched for each competency, once for each source it asked. 101 competencies have never been searched. A search is not material found: the aligned view says what came of it.

none 1–2 3–9 10–29 30+
never 1–2 3–5 6–11 12+
DDigital 94212203420012149086012303134 GGreen 0000000102000010000100010 RResilience & Entrepreneurial 004000000000000010000120000 TDeep Tech 72643202372825212335201140010

T05 Deep Tech · Knowledge · Advanced

Reinforcement Learning

Apply RL principles to decision-making systems

Alignment

3 resources likely to contribute to it or develop it

1 decided at level 2 or 3 (1 develops it) · 2 not decided yet, more likely than not

On a graded sample, 5 pairs were graded 2 or 3 for it, and the map counts 1.

Retrieval

0 resources harvested by searching its terms

Never searched yet.

Search vocabulary

Domain group
reinforcement learningMarkov decision processreward functionpolicy learningQ-learningactor criticagent environmentGRPOgroup relative policy optimizationPPOproximal policy optimizationreward policy

Zenodo and arXiv take the whole group in one query. GitHub takes one phrase at a time and works left to right, stopping once the competency is satisfied — so on that lane this order decides which terms actually run.

The compiled queries
zenodo ("artificial intelligence" OR "machine learning" OR "generative AI" OR "deep learning" OR "reinforcement learning" OR "large language model" OR "AI") AND ("reinforcement learning" OR "Markov decision process" OR "reward function" OR "policy learning" OR "Q-learning" OR "actor critic" OR "agent environment" OR "GRPO" OR "group relative policy optimization" OR "PPO" OR "proximal policy optimization" OR "reward policy")
arxiv (all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"reinforcement learning" OR all:"Markov decision process" OR all:"reward function" OR all:"policy learning" OR all:"Q-learning" OR all:"actor critic" OR all:"agent environment" OR all:"GRPO" OR all:"group relative policy optimization" OR all:"PPO" OR all:"proximal policy optimization" OR all:"reward policy")
github · term 1 AI "reinforcement learning" in:name,description,readme fork:false archived:false
github · term 2 AI "Markov decision process" in:name,description,readme fork:false archived:false
github · term 3 AI "reward function" in:name,description,readme fork:false archived:false
github · term 4 AI "policy learning" in:name,description,readme fork:false archived:false
github · term 5 AI "Q-learning" in:name,description,readme fork:false archived:false
github · term 6 AI "actor critic" in:name,description,readme fork:false archived:false
github · term 7 AI "agent environment" in:name,description,readme fork:false archived:false
github · term 8 AI "GRPO" in:name,description,readme fork:false archived:false
github · term 9 AI "group relative policy optimization" in:name,description,readme fork:false archived:false
github · term 10 AI "PPO" in:name,description,readme fork:false archived:false
github · term 11 AI "proximal policy optimization" in:name,description,readme fork:false archived:false
github · term 12 AI "reward policy" in:name,description,readme fork:false archived:false

Profile edf20909117deb40

Alignment descriptor draft

Outcomes
  • Can formulate a decision problem as a Markov decision process.
  • Can implement and train a reinforcement learning agent such as Q-learning or policy gradient.
  • Can explain exploration, reward design and their pitfalls.
Develops it

Material that teaches reinforcement learning — MDPs, value and policy methods, training agents — with environments the learner codes against.

Only mentions it

RLHF named as a step in training a chatbot, with no reinforcement learning taught.

Alignment terms
reinforcement learningq-learningpolicy gradientreward functionmarkov decision processopenai gymgymnasiumexploration exploitation

Read in a resource's text beside the domain group. They are never compiled into a search.

Neighbours

Descriptor 3a3d76906dd0

How a vocabulary becomes a search

Every candidate must match both concept groups in its title, description or keywords before any content is downloaded. Precision warnings are recorded for audit; they never silently cancel positive evidence.

  1. Official name and description
  2. AI/ML terms and domain terms
  3. Zenodo, arXiv and GitHub syntax
  4. Metadata relevance gate
Mandatory AI/ML group
artificial intelligencemachine learninggenerative AIdeep learningreinforcement learninglarge language modelAI

GitHub's repository search takes one phrase at a time. Measured against the live endpoint, a parenthesised OR group returns nothing at all where each term alone returns thousands, and the 256-character limit rejects a long group outright — so that lane issues one query per term and stops as soon as the competency is satisfied.

coverage-vocabulary-1.3.0+sha256:08ad2cd19868 · catalogue sha256:04970bfc77255b1f

What a descriptor is

A descriptor is how the alignment reads a competency: what a learner who has it can do, what a resource has to do to develop it, what only mentions it, the phrases its subject is written in, its false friends, and how to tell it from the competencies that share its words. The definition above it stays the authority.

Every descriptor is a draft until a partner approves it; 0 of 104 are approved. Read the alignment rubric →

competency-descriptors-1.0.0+sha256:3cc741a61ced