Deep Rl
If you like applying Deep Rl, every challenge here gives you a chance to practice it on a real industry brief.
- ResearchAdvancedNew
Reward Shaping for a Quadruped Locomotion Policy
You receive a quadruped locomotion environment (Isaac Lab or pybullet-quadruped), the previous reward function (5 terms), and a budget of 6 training runs. Design 4 reward varian…
- Reward Shaping
- Ppo
- Locomotion
Robot Learning - CodeAdvancedNew
Actor-Critic for Energy-Storage Dispatch
You receive 3 years of hourly day-ahead price data and a Python simulator that models state of charge, round-trip efficiency, and a 1-day price forecast with documented uncertai…
- Actor Critic
- A2c
- Deep Rl
Reinforcement Learning - ResearchAdvancedNew
Sim-to-Real Domain Randomization for a Mobile Robot
You receive an Isaac Sim navigation environment, a baseline trained policy, a 50-episode real-bench test set (recorded sensor streams + ground truth) for offline policy evaluati…
- Domain Randomization
- Sim To Real
- Robot Navigation
Robot Learning
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
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