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Introduction

D3PE (Deep Data-Driven Policy Evaluation) aims to evaluation a large set of candidate policies by a fix dataset to select best ones.

Supported Algorithms

  • FQE
  • MBOPE
  • IS (WIS)
  • Doubly-Robust

Installation

pip install -e .

or

pip install git+https://agit.ai/Polixir/d3pe.git

Usage

from d3pe.utils.data import OPEDataset
from d3pe.utils.func import get_evaluator_by_name
policy = ... # load your policy, API in d3pe.evaluator.Policy needs to be supported.
dataset = OPEDataset(...) # load your data, see details in `d3pe.utils.data`
evaluator = get_evaluator_by_name('fqe')() # create an ope evaluator, for example FQE
evaluator.initialize(dataset) # initialize the evaluator
ope_score = evaluator(policy) # evaluate the policy

About

D3PE (Deep Data-Driven Policy Evaluation) aims to evaluation a large set of candidate policies from a fixed dataset to select best ones.

Topics

reinforcement-learning

Resources

Readme
Apache-2.0 license
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