danijar

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):

danijar

import flask_restful


class Api(flask_restful.Api):
    """
    Patch Flask-style custom error handling into the Flask-RESTful api class.
    """

    def __init__(self, *args, **kwargs):
        super(Api, self).__init__(*args, **kwargs)

danijar

Keybase proof

I hereby claim:

• I am danijar on github.
• I am danijar (https://keybase.io/danijar) on keybase.
• I have a public key whose fingerprint is 2907 D9B0 3FDD 28B2 6FBB 41B7 E4EB 8875 D9AB FF13

To claim this, I am signing this object:

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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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

danijar

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.

danijar

#!/usr/bin/python3
"""Move or delete directories via your text editor.

Installation:

- Install sh.py via `pip3 install sh`.
- Save this file into a directory in your $PATH, for example `~/bin`.
"""
import argparse
import tempfile

danijar

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

import numpy as np


class Network:

    def __init__(self, num_inputs, num_hidden, num_output,
                 init_weight_scale=0.5):
        self.w1 = np.random.normal(
            0, init_weight_scale, (num_inputs + 1, num_hidden))
        self.w2 = np.random.normal(

danijar

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