ChrissOboa/-Reinforcement-Learning-Homework-Implementing-Q-Learning-and-Deep-Q-Networks
View on GitHub ↗Related repositories →This homework focuses on implementing TD(0), SARSA, Q-Learning, and DQN to train an agent in the CartPole-v1 environment. You will apply the ε-greedy policy, analyze learning curves over 750+ episodes, and compare algorithm performance. Submission includes code, report, plots, and dependencies.
active 2025-02-23 → 2025-03-21 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 27 days from 2025-02-23 to 2025-03-21. Pushes: 2 total, peak 2 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 1 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| ChrissOboa | 2 | 2 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 1 stars here means stars gained during the window, not the repo's star count.