Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
This is the code implementation for the (1) simulation environment, (2) MSBCB framework and (3) all compared baselines presented in the paper: Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising.
1. Code structure
- ./requirements.txt:
including the modules/packages on which the program depends. These pakages should be installed before runing the code bellow. - ./agents:
core code for our MSBCB framework and all compared baseline algorithms. - ./simulation_env:
the code for the virtual environment. - ./replay_buffer:
the code of the experience replay buffers for reinforcement learning algorithms. - ./plot_util:
the code for the tensorboard-logger. - ./figure_for_paper:
the code for drawing figures.
2. Run the code
cd ./agents
python msbcb.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.
python greedy_with_dqn.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.
python greedy_with_ddpg.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.
python greedy_with_ppo.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.
python greedy_with_max_cpr.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.
python contextual_bandit.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.
python constrained_dqn.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.
python constrained_ddpg.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.
python constrained_ppo.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.
python offline_optimal.py --seed=1 --user_num=10000 --budget=12000 --init_cpr_thr=6.