Computer · Data Science
Reinforcement Learning Control Agent (PPO/DQN)
Intermediate 6 weeks 40-60 hours
Train RL agent to control simulated system (robot arm, self-driving car). Implement PPO or DQN algorithm. Show learning curves and final policy performance.
Major
Computer
Focus area
Data Science
Total hours
40-60 hours
What you'll need
- Software only - Python, PyTorch, OpenAI Gym, MuJoCo sim, tensorboard
Steps
- Pick a simulated environment in Gym or MuJoCo.
- Implement or set up PPO or DQN in PyTorch.
- Train the agent and track learning curves in TensorBoard.
- Tune hyperparameters to improve performance.
- Record the final policy in action.
- Document learning curves and results for your portfolio.
What to photograph for your portfolio
- Learning curve graph
- final policy performance video
- reward accumulation plots
- hyperparameter tuning results
Resume bullet starters
Copy one, then swap in your own numbers.
Designed and built a reinforcement learning control agent using Python, reinforcement learning, PyTorch/TensorFlow
Applied simulation environments (Gym/MuJoCo) and visualization to implement, test, and validate the system end-to-end
Quantified performance with [insert your result, such as accuracy, error reduction, response time, or load capacity] after iterative tuning
Skills you'll show off
Pythonreinforcement learningPyTorch/TensorFlowsimulation environments (Gym/MuJoCo)visualization