Blur view of reacher pybullet env

I trained the ReacherBulletEnv-v0 using this repository, but when I run the enjoy.py file, I get a very blur environment. How to correct this?

The config and version of packages in my environment are given below:

Package                        Version           
------------------------------ -------------------
absl-py                        0.11.0
alembic                        1.4.3
astroid                        2.4.2
astunparse                     1.6.3
atari-py                       0.2.6
attrs                          20.3.0
azure                          1.0.3
azure-common                   1.1.26
azure-mgmt                     0.20.2
azure-mgmt-common              0.20.0
azure-mgmt-compute             0.20.1
azure-mgmt-network             0.20.1
azure-mgmt-nspkg               3.0.2
azure-mgmt-resource            0.20.1
azure-mgmt-storage             0.20.0
azure-nspkg                    3.0.2
azure-servicebus               0.20.1
azure-servicemanagement-legacy 0.20.2
azure-storage                  0.20.3
backcall                       0.2.0
baselines                      0.1.4            
cachetools                     4.1.1
certifi                        2020.6.20
cffi                           1.14.3
chardet                        3.0.4
click                          7.1.2
cliff                          3.5.0
cloudpickle                    1.6.0
cmaes                          0.7.0
cmd2                           1.4.0
colorama                       0.4.4
colorlog                       4.6.2
cycler                         0.10.0
Cython                         0.29.21
dataclasses                    0.6
decorator                      4.4.2
dill                           0.3.3
future                         0.18.2
gast                           0.3.3
glfw                           2.0.0
google-auth                    1.23.0
google-auth-oauthlib           0.4.2
google-pasta                   0.2.0
grpcio                         1.33.2
gym                            0.17.3
h5py                           2.10.0
idna                           2.10
imageio                        2.9.0
iniconfig                      1.1.1
ipdb                           0.13.4
ipython                        7.18.1
ipython-genutils               0.2.0
isort                          5.5.4
jedi                           0.17.2
joblib                         0.17.0
Keras-Preprocessing            1.1.2
kiwisolver                     1.2.0
lazy-object-proxy              1.4.3
llvmlite                       0.34.0
lockfile                       0.12.2
Mako                           1.1.3
Markdown                       3.3.3
MarkupSafe                     1.1.1
matplotlib                     3.3.2
mccabe                         0.6.1
more-itertools                 8.5.0
mpi4py                         3.0.3
numba                          0.51.2
numpy                          1.19.2
oauthlib                       3.1.0
opencv-python                  4.4.0.44
opt-einsum                     3.3.0
optuna                         2.3.0
packaging                      20.7
pandas                         1.1.2
parso                          0.7.1
pbr                            5.5.1
pexpect                        4.8.0
pickleshare                    0.7.5
Pillow                         7.2.0
pip                            20.2.2
pluggy                         0.13.1
prettytable                    0.7.2
progressbar2                   3.53.1
prompt-toolkit                 3.0.8
protobuf                       3.13.0
psutil                         5.7.3
ptyprocess                     0.6.0
py                             1.9.0
py-dateutil                    2.2
pyarrow                        2.0.0
pyasn1                         0.4.8
pyasn1-modules                 0.2.8
pybullet                       3.0.4
pybullet-robot-envs            0.0.1              
pycparser                      2.20
pyglet                         1.5.0
Pygments                       2.7.2
pylint                         2.6.0
pyparsing                      2.4.7
pyperclip                      1.8.1
pytest                         6.1.2
python-dateutil                2.8.1
python-editor                  1.0.4
python-utils                   2.4.0
pytz                           2020.1
PyYAML                         5.3.1
pyzmq                          20.0.0
requests                       2.25.0
requests-oauthlib              1.3.0
rsa                            4.6
scipy                          1.5.2
seaborn                        0.11.0
setuptools                     49.6.0.post20200925
six                            1.15.0
SQLAlchemy                     1.3.20
stable-baselines               2.10.1
stable-baselines3              0.10.0
stevedore                      3.3.0
tensorboard                    2.4.0
tensorboard-plugin-wit         1.7.0
tensorboardX                   2.1
tensorflow                     2.3.1
tensorflow-estimator           2.3.0
termcolor                      1.1.0
toml                           0.10.1
torch                          1.7.0
tqdm                           4.51.0
traitlets                      5.0.5
typing-extensions              3.7.4.3
urllib3                        1.26.2
wcwidth                        0.2.5
Werkzeug                       1.0.1
wheel                          0.35.1
wrapt                          1.12.1
zmq                            0.0.0

[erwincoumans]

The blurry image is likely due to the simple cpu TinyRenderer instead of using hardware OpenGL 3. Yes, it is a good idea to call env.render() before the first env.reset(). This is a special feature that enables hardware rendering, since the Gym API has no proper way to add options before starting the env. Can this be made default, if ‘enjoying’ the pybullet environments?

Another option is to run a pybullet server, which will be used for hardware OpenGL rendering in a different terminal, leave it running, and then enjoy the environment/create a video. A PyBullet env will first try to detect such server and connect over shared memory. Don’t resize/close while creating the video.

python -m pybullet_utils.runServer

This is how it should look like (AntBullet-v0), note the reflection and shadows etc:

[chisarie]

Also other pybullet envs have some distortion. But I just found out that doing env.render() before env.reset() solves both the weird color and the distortion. The image is still blurry / low quality, but I guess this is only a setting thing. In case the OP is still interested, this is my script:

import time
import gym
import pybullet_envs  # noqa: F401
from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv

env_id = "ReacherBulletEnv-v0"
video_folder = "logs/videos/"
video_length = 100

env = DummyVecEnv([lambda: gym.make(env_id)])
env.render()

# Record the video starting at the first step
env = VecVideoRecorder(
    env,
    video_folder,
    record_video_trigger=lambda x: x == 0,
    video_length=video_length,
    name_prefix="random-agent-{}".format(env_id),
)

obs = env.reset()
for _ in range(video_length + 1):
    action = [env.action_space.sample()]
    obs, _, _, _ = env.step(action)
# Save the video
env.close()