tests on ubuntu18.04

* updated benchmark pages with final rewards

* use htmlpreview to render pages

* use htmlpreview to render pages

* use htmlpreview to render pages

* updated README to reflect ppo1 being obsolete

* removed navbars from published benchmark pages

* fixed link in README

.gitignore

@@ -5,7 +5,6 @@
 .pytest_cache
 .DS_Store
 .idea
-.coverage
 
 # Setuptools distribution and build folders.
 /dist/

Dockerfile

@@ -1,4 +1,4 @@
-FROM ubuntu:16.04
+FROM ubuntu:18.04
 
 RUN apt-get -y update && apt-get -y install git wget python-dev python3-dev libopenmpi-dev python-pip zlib1g-dev cmake python-opencv
 ENV CODE_DIR /root/code

README.md

@@ -112,10 +112,6 @@ This should get to the mean reward per episode about 5k. To load and visualize t
 *NOTE:* At the moment Mujoco training uses VecNormalize wrapper for the environment which is not being saved correctly; so loading the models trained on Mujoco will not work well if the environment is recreated. If necessary, you can work around that by replacing RunningMeanStd by TfRunningMeanStd in [baselines/common/vec_env/vec_normalize.py](baselines/common/vec_env/vec_normalize.py#L12). This way, mean and std of environment normalizing wrapper will be saved in tensorflow variables and included in the model file; however, training is slower that way - hence not including it by default
 
 
-
-
-
-
 ## Subpackages
 
 - [A2C](baselines/a2c)
@@ -125,10 +121,19 @@ This should get to the mean reward per episode about 5k. To load and visualize t
 - [DQN](baselines/deepq)
 - [GAIL](baselines/gail)
 - [HER](baselines/her)
-- [PPO1](baselines/ppo1) (Multi-CPU using MPI)
-- [PPO2](baselines/ppo2) (Optimized for GPU)
+- [PPO1](baselines/ppo1) (obsolete version, left here temporarily)
+- [PPO2](baselines/ppo2) 
 - [TRPO](baselines/trpo_mpi)
 
+
+
+## Benchmarks
+Results of benchmarks on Mujoco (1M timesteps) and Atari (10M timesteps) are available 
+[here for Mujoco](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_mujoco1M.htm) 
+and
+[here for Atari](https://htmlpreview.github.com/?https://github.com/openai/baselines/blob/master/benchmarks_atari10M.htm) 
+respectively. Note that these results may be not on the latest version of the code, particular commit hash with which results were obtained is specified on the benchmarks page. 
+
 To cite this repository in publications:
 
     @misc{baselines,

baselines/deepq/README.md

@@ -32,7 +32,7 @@ In particular notice that once `deepq.learn` finishes training it returns `act`
 
 
 - [baselines/deepq/experiments/custom_cartpole.py](experiments/custom_cartpole.py) - Cartpole training with more fine grained control over the internals of DQN algorithm.
-- [baselines/deepq/experiments/atari/train.py](experiments/atari/train.py) - more robust setup for training at scale.
+- [baselines/deepq/experiments/run_atari.py](experiments/run_atari.py) - more robust setup for training at scale.
 
 
 ##### Download a pretrained Atari agent

setup.py

@@ -25,8 +25,7 @@ setup(name='baselines',
       extras_require={
         'test': [
             'filelock',
-            'pytest',
-            'pytest-cov',
+            'pytest'
         ]
       },
       description='OpenAI baselines: high quality implementations of reinforcement learning algorithms',