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Neural Logic Reinforcement Learing

Implementaion of Neural Logic Reinforcement learning and several benchmarks. Neural Logic Reinforcement Learning uses deep reinforcement leanring methods to train a differential indutive logic progamming architecture, obtaining explainable and generalizable policies. Paper accepted by ICML2019.

Enviornments

Developed in python2.7, Linux enviornment.

Dependencies

  • numpy
  • tensorflow (1.11)

User Guide

  • use main.py to run the experiments
  • --mode= to specify the running mode, can be "train" or "generalize", where generalize means to run a generalization test.
  • --task= to specify the task, can be "stack", "unstack", "on" or "cliffwalking".
  • --algo to specify agent type, can be "DILP", "NN" or "Random"
  • --name to specify the id of this run.
  • for example: python main.py --mode=train --algo=DILP --task=unstack --name=ICMLtest