* fix some problems of tensorboard

* yapf

* fix the compatibility issue

* fix the comment issue

* support paddle 1.5.1 and replace PE with compiler

* yapf&copyright

* yapf

* fix the teamcity problem

* fix the teamcity problem

* fix comment

* only support paddle 1.5.1

* Cmake

* fix comment

* fix the compatibility issue

* fix the comment issue

* remove previous algorithms; use the forward function in parl.Model

* remove abundant lines

* yapf

* intra-version: move parl.framework into parl.core.fluid

* add folder: parl.core

* remove former test folders

* yapf

* yapf0.24

* Update README.cn.md

* Update README.md

* Update README.md

* Update README.cn.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* refine A2C example

* fix unittest in python2; fix codestyle

* fix codestyle

* refine comment

* add A2C benchmark; add more information in PyPI homepage

* filter picture in PyPI homepage

* fix typo

* Update README.md

* Update README.md

* Update README.md

* soft depend on fluid; add module to monitor client status

* improve performance of IMPALA example

* fix bug of some client cannot exit normally

* refine comment

* .

* add IMPALA algorithm and some common utils

* update README.md

* refactor files structure of impala algorithm; seperate numpy utils from utils

* add hyper parameter scheduler module; add entropy and lr scheduler in impala

* clip reward in atari wrapper instead of learner side; fix codestyle

* add benchmark result of impala; refine code of impala example; add obs_format in atari_wrappers

* Update README.md

* add a3c algorithm, A2C example and rl_utils

* require training in single gpu/cpu

* only check cpu/gpu num in learner

* refine Readme

* update impala benchmark picture; update Readme

* add benchmark result of A2C

* move get_params/set_params in agent_base

* add GA3C example

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* refine Readme

* add benchmark

* add default safe eps in numpy logp calculation

* refine document; make unittest stable

* add IMPALA algorithm and some common utils

* update README.md

* refactor files structure of impala algorithm; seperate numpy utils from utils

* add hyper parameter scheduler module; add entropy and lr scheduler in impala

* clip reward in atari wrapper instead of learner side; fix codestyle

* add benchmark result of impala; refine code of impala example; add obs_format in atari_wrappers

* Update README.md

* add a3c algorithm, A2C example and rl_utils

* require training in single gpu/cpu

* only check cpu/gpu num in learner

* refine Readme

* update impala benchmark picture; update Readme

* add benchmark result of A2C

* move get_params/set_params in agent_base

* fix shell script cannot run in ubuntu

* refine comment and document

* Update README.md

* Update README.md