Deep-Reinforcement-Learning
Deep Reinforcement Learning from mathematical foundations to PyTorch implementations: rigorous proofs, step-by-step derivations of objectives and gradient estimators, and reproducible experiments on Gymnasium and MuJoCo benchmarks spanning policy gradients, actor-critic methods, value-based learning, and continuous control
What XERJ indexed
Snapshot
The whole repository at commit fb05f0c143 (2026-10-27). master is 5 commits ahead.
Selected work
“Structured buffers”. 3 files, 469 lines in src/deeprl, 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 2026-10-29. 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
a2cactor-criticdeep-reinforcement-learninggymnasiumpolicy-gradientreinforcement-learningreinforcement-learning-algorithmsrobot-learning
Example questions
- How does Deep-Reinforcement-Learning implement a2c?
- Where does Deep-Reinforcement-Learning handle “Structured buffers”?
- What does src/deeprl do in Deep-Reinforcement-Learning?
Inside the indexed corpus
- 41 files, 6.2 MB in total, about 153.7 KB per file.
- The selected commit changed 3 of those files (7.3%).
- On GitHub since 2026-08-03, last push 2026-10-29, 45 commits on master.
- The pin is 5 commits behind master; the corpus answers from the pinned code, not the newest.
Licence on GitHub and in the corpus
GitHub reported MIT for SaiSampathKedari/Deep-Reinforcement-Learning on 2026-10-31, and the LICENSE file at commit fb05f0c143 was reviewed as MIT. They match, so answers from this corpus carry the same terms as the live repository.
Contributors
Sai Sampath Kedari (@SaiSampathKedari) has 1 selected repository on the hub.
Using XERJ with Deep-Reinforcement-Learning
- Index a reference library for a coding agent
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The prompt
Paste it into your AI agent. It clones SaiSampathKedari/Deep-Reinforcement-Learning at the pinned commit and indexes it on your machine.
Search the GitHub repository SaiSampathKedari/Deep-Reinforcement-Learning with XERJ (https://hub.xerj.org/corpus/saisampathkedari--deep-reinforcement-learning). 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/SaiSampathKedari/Deep-Reinforcement-Learning Deep-Reinforcement-Learning && git -C Deep-Reinforcement-Learning checkout fb05f0c1431ad19c419b7e8720e3453f5c1a4d8b 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./Deep-Reinforcement-Learning --url http://127.0.0.1:9200 --prefix deep-reinforcement-learn --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: MIT. Keep the copyright notice and credit the authors.
Related
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Stats: GitHub API, fetched 2026-10-31. Snapshot: the git tree at the pinned commit. Selected work and consent: the XERJ registry.