* liftsim a2c baseline

* update readme

* compatible with different os

* empty

* refine comments

* remove unnecessary assertion; add tensorboard guide

* remove unnecessary assertion

* update parl dependence of A2C

* Limit impala to single GPU training

* refine comment of scheduler

* refine comment

* add learning curve for ES

* add learning curve for ES

* support new APIs of the cluster

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* rename learner.py

* Update README.md

* Update README.md

* Update README.cn.md

* Update README.md

* Update README.cn.md

* Update README.md

* ES example

* refine settings

* fix yapf

* refine documentation; remove csv logger

* fix bug

* merge learner.py and train.py; add version requirements of gym and atari_py

* unify actor num

* first pr

* start a worker when the master is started.

* First PR & Fix logger bugs.

* update docs for a2c, impala and ga3c

* update doc

* yapf modification

* update logger

* yapf correct

* yapf

* setup.py

* old setup.py

* worker 86

* fix the compatibility issue

* fix the comment issue

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

* add folder: parl.core

* remove former test folders

* yapf

* yapf0.24

* refine A2C example

* fix unittest in python2; fix codestyle

* fix codestyle

* 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

* fix shell script cannot run in ubuntu

* refine comment and document

* Update README.md

* Update README.md

* 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