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Residual Loss Prediction: Reinforcement Learning with No Incremental Feedback

An implementation for our paper: residual loss prediction: reinforcement learning with no incremental feedback. If you find this code useful in your research, please consider citing:

    @article{daume2018residual,
      title={Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback},
      author={Daum{\'e} III, Hal and Langford, John and Sharaf, Amr},
      journal={International Conference on Learning Representations (ICLR)},
      year={2018}
    }

To run:

    python run_reslope.py grid::0.05::0.9::0.99 blols::mtr::multidev::bootstrap adam 0.01 bag_size=3 --dynet-seed 3

This command will run reslope on the grid world environment with the following parameters:

  1. Run reslope in multiple deviation mode
  2. Use Multi-task regression (MTR) contextual bandit oracle, MTR reduces contextual bandit costs directly to importance-weighted regression.
  3. Use bootstrap exploration for the contexual bandit oracle with a bag size of three policies.

The code has a dependency on the macarico github repo, and the dynet branch is required to run this code base.

Examples

This command will run the reinforce algorithm on the blackjack environment:

    python run_reslope.py blackjack reinforce adam 0.01 --dynet-seed 90210

This command will run the PPO algorithm on the cartpole enviroment:

    python run_reslope.py cartpole ppo::k=3::n=20::m=1 adam 0.01 --dynet-seed 3

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