Ppo
If you like applying Ppo, every challenge here gives you a chance to practice it on a real industry brief.
- CodeExpertNew
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 - ResearchExpertNew
Curriculum RL for a Simulated Drone Inspection Task
You receive a PyBullet-based wind-turbine inspection simulator with parameterizable wind, blade orientation, and sensor noise. Design a 3-stage curriculum: (1) hover near a stat…
- Ppo
- Curriculum Learning
- Deep Rl
Reinforcement Learning - ResearchExpertNew
Train Cooperative Agents with Multi-Agent RL
Pick an open multi-agent environment (PettingZoo's MPE 'simple_spread', Overcooked-AI, or SMAC). Implement or wrap three methods: IPPO (independent PPO per agent), MAPPO (centra…
- Multi Agent Reinforcement Learning
- Ppo
- Pytorch
Multi-Agent Systems - CodeExpertNew
PPO Alignment Loop with a Pretrained Reward Model
You receive a small open-weights base model (around 7B), a previously trained reward model, and 5,000 prompts (no responses) for PPO rollouts. Run PPO with TRL's PPOTrainer for …
- Rlhf
- Ppo
- Reward Hacking
Machine Learning from Human Preferences (RLHF and Alignment) 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
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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