Business
Economics & Policy Analysis Challenges
Economics & Policy Analysis challenges put you inside the work of explaining how markets and policies move people. You'll develop skills in Supply & Demand, reading Economic Data, finding Economic Data Sources, and applying Game Theory to strategic situations.
From there you'll handle the harder edges — Causal inference, Policy impact evaluation, Behavioral economics, and Geopolitical risk modeling — working with Linear Regression, Hypothesis Testing, and Structural econometric modeling the way analysts actually do. Each challenge you solve earns a verified credential you can share with recruiters.
Recommended Challenges
· Policy impact evaluation Clear- AnalysisIntermediateNew
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
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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…
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Behavior Cloning for a Pick-and-Place Manipulator
You receive 200 human teleoperated demonstrations (state + action trajectories) of picking 8 small electronic components from a tray and placing them at marked locations in a ro…
- Behavior Cloning
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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 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
- CodeIntermediateNew
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
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Reinforcement Learning - ResearchSeniorNew
Quantify Sim-to-Real Gap for a Warehouse Manipulation Policy
You receive a trained pick-and-place policy (PyTorch), the simulation env (Isaac Lab), and access to a real-arm rig (or recorded teleop episodes if hardware is unavailable). Def…
- Sim To Real
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Robot Perception and Autonomy - CodeIntermediateNew
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 - CodeIntermediateNew
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 - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- ResearchSeniorNew
Diffusion-Policy Imitation for Bimanual Cooking Tasks
You receive 300 teleoperated demonstrations of a bimanual pour-and-stir task in a Robomimic-style simulator, deliberately including 2 valid solution modes per task (left-pour-ri…
- Diffusion Policies
- Imitation Learning
- Multimodal Action Distributions
Robot Learning - CodeSeniorNew
Train a Reinforcement-Learning Policy for Drone Obstacle Avoidance
You receive a custom Gymnasium drone-flight environment (provided), a baseline hand-engineered controller, and a target evaluation suite covering 4 obstacle densities. Train a P…
- Reinforcement Learning
- Ppo
- Robotics Simulation
Advanced Robotics - ResearchIntermediateNew
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 - CodeSeniorNew
Offline RL for Robot-Arm Skill Reuse
You receive 5,000 logged trajectories (state, action, reward, next-state) across 12 tasks, with 9 tasks for training and 3 held out. Train an offline RL algorithm (CQL or IQL re…
- Offline Rl
- Conservative Q Learning
- Skill Reuse
Robot Learning Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- CodeBeginnerNew
Tabular Q-Learning for Warehouse Slotting
You receive a Python discrete-event simulator with state encoded as a 12-dimensional categorical vector (around 8,000 reachable states) and 6 possible slotting actions, plus 2 y…
- Tabular Rl
- Q Learning
- Epsilon Greedy
Reinforcement 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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