metadata
license: unknown
Intro
It's RL (Reinforcement Learning) PPO (Proximal Policy Optimization) model for DOOH DSP Bidder problem. The model should respect 4 rules:
- even pacing over time
- desired publishers distribution (which can be different from publishers distribution in raw bid requests flow).
- desired venue types distribution (which can be different from venue types distribution in raw bid requests flow).
- desired household sizes distribution (which can be different from household sizes distribution in raw bid requests flow).
Requirements.txt
torch==2.10.0
matplotlib==3.10.8
ipython==8.0.0
torchrl==0.11.1
tensordict==0.11.0
numpy==2.4.2
pandas==2.3.3
Training process
Data flow
Python all-in-one files
- dsp_bidder_4_training.py - training
- dsp_bidder_4_environment.py - environment
- dsp_bidder_4_inference.py - inference
- dsp_bidder_4_inference_once.py - inference single bid request