PyTorch implementation of the Offline Reinforcement Learning algorithm CQL. Includes the versions DQN-CQL and SAC-CQL for discrete and continuous action spaces.
active 2023-08-18 → 2025-12-19 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 855 days from 2023-08-18 to 2025-12-19. Pushes: 1 total, peak 1 in a day. Pull requests: 2 total, peak 2 in a day. Issues: 4 total, peak 2 in a day. Comments: 4 total, peak 2 in a day. Stars: 75 total, peak 3 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
Recent activity
Latest issues, pull requests and releases
- Pull request#8NA93522024-05-06 15:11
- Pull request#8NA93522024-05-06 15:10
- Issue comment#4sohanglal2024-02-21 06:01Offline CQL performance on Hopper expert env
- Issue comment#4Jcillo5072024-02-21 00:10Offline CQL performance on Hopper expert env
- Issue#7britisony2023-10-26 05:48Training for discrete lunar lander envirionment
- Issue#7britisony2023-10-26 03:40Training for discrete lunar lander envirionment
- Issue#2BY5712023-08-18 18:10CQL Loss
- Issue comment#5BY5712023-08-18 18:10Potential typo in the CQL implementation
- Issue#5BY5712023-08-18 18:10Potential typo in the CQL implementation
- Issue comment#2BY5712023-08-18 18:07CQL Loss
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 75 stars here means stars gained during the window, not the repo's star count.