sichkar-valentyn/Reinforcement_Learning_in_Python

Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa

513 117 +0/wk

GitHub

maze-algorithms maze-solver obstacle-avoidance path-planning q-learning q-learning-vs-sarsa reinforcement-learning reinforcement-learning-algorithms rl-agents rl-algorithms rl-emulator rl-environment

Trend 0

Star & Fork Trend (23 data points)

Stars

Forks

Multi-Source Signals

GitHub

stars 513

forks 117

Growth Velocity

sichkar-valentyn/Reinforcement_Learning_in_Python has +0 stars this period . Velocity data will be available after more historical data is collected.

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Signal-backed technical analysis will be available soon.

Metric	Reinforcement_Learning_in_Python	tbp.monty	OmniNet	gym-mtsim
Stars	513	513	513	514
Forks	117	297	59	122
Weekly Growth	+0	+1	+0	+0
Language	Python	Python	Python	Python
Sources	1	1	1	1
License	MIT	MIT	N/A	MIT

Capability Radar vs tbp.monty

Reinforcement_Learning_in_Python

tbp.monty

Maintenance Activity 0

Last code push 1445 days ago.

Community Engagement 100

Fork-to-star ratio: 22.8%. Active community forking and contributing.

Issue Burden 70

Issue data not yet available.

Growth Momentum 30

No measurable growth in the current period (first-day cold start expected).

License Clarity 95

Licensed under MIT. Permissive — safe for commercial use.

Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.