ROCCYK/RL_Space_Invaders
40
An interactive reinforcement learning project where agents trained using Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) play Atari's classic Space Invaders. The project includes a Streamlit app to visualize and compare the performance of both agents.
What's novel
An interactive reinforcement learning project where agents trained using Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) play Atari's classic Space Invaders. The project includes a Streamlit app to visualize and compare the performance of both agents.
Score Breakdown
Innovation
2 (25%)
Craft
34 (35%)
Traction
9 (15%)
Scope
46 (25%)
Signal breakdown
Innovation
Not Fork+1
Code Novelty+0
Unique Niche+1
Concept Novelty+0
Craft
Ci-3
Tests-4
Polish+0
Releases+0
Has License+0
Code Quality+12
Readme Quality+12
Recent Activity+7
Structure Quality+5
Commit Consistency+0
Has Dependency Mgmt+5
Traction
Forks+0
Stars+6
Hn Points+0
Watchers+3
Early Traction+0
Devto Reactions+0
Community Contribs+0
Scope
Commits+5
Languages+3
Subsystems+5
Bloat Penalty+0
Completeness+6
Contributors+5
Authored Files+8
Readme Code Match+3
Architecture Depth+3
Implementation Depth+8
Evidence
Commits
12
Contributors
1
Files
17
Active weeks
2
TestsCI/CDREADMELicenseContributing
Repository
Language
Python
Stars
1
Forks
0
License
—