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Code of ICML-2020 paper Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

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tjuHaoXiaotian/ICML-2020-MSBCB

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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.

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Code of ICML-2020 paper Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

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