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• I am danijar on github.
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import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt


def gaussian_blur(image, diameter):
  padding = [[0, 0]] + [[(diameter) // 2, (diameter - 1) // 2]] * 2 + [[0, 0]]
  diameter = tf.to_float(diameter)
  filter_ = tf.range(-(diameter - 1) // 2, (diameter - 1) // 2 + 1)
  filter_ = tf.exp(-0.5 * filter_ ** 2 / (diameter / 4) ** 2)  # 2 stds.

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import argparse
import os

import agents
import gym
import gym.spaces
import numpy as np
import tensorflow as tf
from dm_control import suite  # Must be imported after TensorFlow.

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# Full example for my blog post at:
# https://danijar.com/building-variational-auto-encoders-in-tensorflow/

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

tfd = tf.contrib.distributions

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import functools

import tensorflow as tf


class share_variables(object):

  def __init__(self, callable_):
    self._callable = callable_
    self._wrappers = {}

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def minitaur_config():
  # General
  algorithm = ppo.PPOAlgorithm
  num_agents = 10
  eval_episodes = 30
  use_gpu = False
  # Environment
  env = 'MinitaurBulletEnv-v0'
  max_length = 1000
  steps = 1e7  # 10M

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python -c "import gym,time;d=10000;e=gym.make('Ant-v1');s=time.time();e.reset();[e.reset() if e.step(e.action_space.sample())[2] else 0 for _ in range(d)];print(d/(time.time()-s),'FPS')"

danijar

TensorFlow Agents PyBullet Usage Example

This example shows how to install TensorFlow agents and use it on custom environments, such as the environments that come with PyBullet.

It works for both Python 3 and Python 2. Just replace pip3 and python3 with pip2 and python2.

Set up the dependencies:

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf


class GRU(tf.contrib.rnn.RNNCell):