* 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

* make the quickstart more compact

* remove args in the main function

* yapf

* add gif

* remove render

* Update README.md

* Update README.md

* Update README.md

* 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

* 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 api set_params/get_params in Model; add Interface of Network and LayerFunc to solve circular imports; refine parameter_names api of Model

* remove licence in third party code; remove interface of Network and LayerFunc; move get_parameter_pairs and get_parameter_names api to Network

* refine comment

* refine commment

* Update README.md

* Update train.py

* Update README.md

* Update agent_base.py

* Update train.py

* Update train.py

* Update train.py

* Update model_base.py

* Update README.md

* Update README.md

* fix PPO bug; add more benchmark result

* refine code

* update benchmark of PPO, after fix bug

* refine code

* add more dqn benchmark result; unify train scripts

* resize benchmark picture

* resize benchmark picture, refine comments of args

* change dependence, mujoco only support python3 now

* remove not used files, add benchmark for DQN and DDPG, add Parameters management Readme

* Update README.md

* Update README.md

* add parl dependence in examples, use np shuffle instead of sklean

* fix codestyle

* refine readme of nips example

* fix bug

* fix code style

* Update README.md

* Update README.md

* Update README.md

* refine document and remove outdated design doc

* Update README.md

* Update README.md

* refine comment

* release version 1.0

* gif of examples

* Update README.md

* update Readme

* Update README.md

* add experimental results

* NeurIPS2018-AI-for-Prosthetics-Challenge training code

* remove model_zoo, provide download link

* remove model_zoo, provide download link

* add restore_from_one_head api, refine README, fix logger bug

* fix test bug

* fix rpm bug, refine ddpg train script

* fix rpm bug, refine Readme

* add PPO example

* Update Readme

* Update Readme

* fix codestyle

* Update Readme

* refine action mapping

* add more unitest case

* remove unnecessary params initialize, add more comments, add benchmark result

* rename

* remove PARL dependence in readme of examples

* add DDPG example, fix some tiny bug

* add license

* unify code structure

* unify code structure

* refine gputils, fix seed in QuickStart

* use white noise in DDPG

* fix codestyle

* add a episode to show the final test reward

* make code more clear

* add QuickStart example, refine DQN example

* add examples link

* refine the naming, and add quick start training result

* add DQN example, add Agent unittest

* refine readme

* refine  code

* simplify code

* add testing module of NeurIPS2018-AI-for-Prosthetics-Challenge, add dependencies of setup

* add copyright

* add google drive link

* fix depedencie

* refine setup

code will be released before 31.October (#20)