Policy Evaluation
If you like applying Policy Evaluation, every challenge here gives you a chance to practice it on a real industry brief.
- 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 - CodeAdvancedNew
Plan Inventory Replenishment as an MDP for an E-Commerce AI Startup
You receive 18 months of daily demand for 50 representative SKUs at one warehouse plus lead-time and unit-cost data. For one SKU at a time, formulate an MDP with state = (on-han…
- Mdp Modeling
- Value Iteration
- Dynamic Programming
Decision Making Under Uncertainty - ResearchAdvancedNew
Train a Reinforcement-Learning Locomotion Policy for a Quadruped
You receive a configured Isaac Lab environment for the quadruped, a baseline PPO trainer, and a set of 8 trip-hazard / slip stress scenarios. Train the policy for a budget of ab…
- Reinforcement Learning
- Locomotion
- Domain Randomization
Robotics - 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 Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- 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 - AnalysisAdvancedNew
Frame an Energy-Storage Dispatch Decision as a Bayesian Decision Problem
You receive 2 years of hourly spot-price data, 2 years of wind generation data, and a manufacturer's battery degradation model. Frame dispatch as a Bayesian decision problem: mo…
- Bayesian Decision Theory
- Price Modeling
- Back Testing
Decision Making Under Uncertainty - CodeAdvancedNew
Run a Monte Carlo Tree Search Strategy for a Robotics Pick-and-Place Task
You receive a simulator of the pick-and-place task: a bin with 10 randomly-placed parts, an action space of which part to pick next, and a reward = parts picked per minute with …
- Monte Carlo Tree Search
- Planning
- Simulation
Decision Making Under Uncertainty
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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