danijar

algorithms:
- config:
  | frame_skip: 6
  | history: 6
  | replay_capacity: 5e4
  name: DQN
  train_steps: 200000
  type: DQN
envs:
- SimpleGather-v0

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

"""Character based language modeling with multi-layer GRUs.

To train the model:

  python3 tf_char_rnn.py --mode training \
      --logdir path/to/logdir --corpus path/to/corpus.txt

To generate text from seed words:

  python3 tf_char_rnn.py --mode sampling \

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

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

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:

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

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 functools

import tensorflow as tf


class share_variables(object):

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

danijar

# 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