MASA-Safe-RL
MASA-Safe-RL: A safe reinforcement library for providing a common interface for different constraints and environments
What XERJ indexed
Snapshot
The whole repository at commit c8e93cf8b0 (2026-10-14). main is 29 commits ahead.
Selected work
“drop tensorflow probability dependencies with native jax implementation”. 6 files, 238 lines in masa, the project root.
Licence
The repository
LICENSE file checked at the pinned commit; the author agreed to inclusion.
The XERJ corpus
The author agreed by email on 2026-10-27. Same terms as the repository. Answers name the repo, the commit and the file:line.
selected for the XERJ Corpus Hublicence clearedsuggested use: adapt with attribution
benchmarkconstrained-reinforcement-learningconstraint-satisfaction-problemdeep-learningdeep-reinforcement-learninggymnasiumjaxmachine-learning
Example questions
- How does MASA-Safe-RL implement benchmark?
- What does masa do in MASA-Safe-RL?
- What changed in MASA-Safe-RL for “drop tensorflow probability dependencies”?
Inside the indexed corpus
- Languages by size: Jupyter Notebook 72.1%, Python 27.9%.
- On GitHub since 2025-10-14, last push 2026-10-31, 620 commits on main.
- The pin is 29 commits behind main; the corpus answers from the pinned code, not the newest.
Licence on GitHub and in the corpus
GitHub reported Apache-2.0 for sacktock/MASA-Safe-RL on 2026-10-31, and the LICENSE file at commit c8e93cf8b0 was reviewed as Apache-2.0. They match, so answers from this corpus carry the same terms as the live repository.
Contributors
Alex Goodall (@sacktock) has 1 selected repository on the hub.
Using XERJ with MASA-Safe-RL
- Search Jupyter notebooks (.ipynb) and the .py beside them Jupyter Notebook is 72.1% of the code by size.
- RAG without a vector database: one hybrid index The hub files it under AI, prompts and agents.
- Index a reference library for a coding agent
- ripgrep vs a code index for coding agents: 30 tasks
The prompt
Paste it into your AI agent. It clones sacktock/MASA-Safe-RL at the pinned commit and indexes it on your machine.
Search the GitHub repository sacktock/MASA-Safe-RL with XERJ (https://hub.xerj.org/corpus/sacktock--masa-safe-rl). Run these in a shell, in order, and check each result before the next: 1. xerj --help (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh 2. git clone https://github.com/sacktock/MASA-Safe-RL MASA-Safe-RL && git -C MASA-Safe-RL checkout c8e93cf8b099a259aef5e3c7b56ff5f4abd4d963 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./MASA-Safe-RL --url http://127.0.0.1:9200 --prefix masa-safe-rl --progress plain 5. xerj autoindex map --url http://127.0.0.1:9200 Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt Licence: Apache-2.0. Keep the copyright notice and credit the authors.
Related
Alex GoodallAI, prompts and agents corporacognitaargo-workflowshuggingface-hub-docsjamilhammoudeh/vbarSaiSampathKedari/Deep-Reinforcement-Learningray-project/ray
Stats: GitHub API, fetched 2026-10-31. Selected work and consent: the XERJ registry.