NLRL
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Source code of Neural Logic Reinforcement Learning (https://arxiv.org/abs/1904.10729)
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