Example for using TensorFlow Agents with custom environments

agents_pybullet_example.md

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:

sudo pip3 install -U tensorflow
sudo pip3 install -U gym
sudo pip3 install -U ruamel.yaml

Install TensorFlow agents from PyPi:

sudo pip3 install agents

Instead, if you want to hack the code, clone the repository:

git clone [email protected]:tensorflow/agents.git
cd agents
sudo python3 setup.py develop

Run the script to train an agent:

python3 agents_example.py --mode train --logdir ~/experiments/agents  --config pybullet_ant

Launch TensorBoard to visualize training:

tensorboard --logdir=~/experiments/agents

During or after training, use this script to render videos:

python3 agents_example.py visualize ~/experiments/agents/ppo_on_pybullet_ant

agents_pybullet_example.py

"""Template for using TensorFlow agents."""

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

import argparse
import datetime
import logging
import os

import agents
import pybullet_envs  #  To make AntBulletEnv-v0 available.
import tensorflow as tf


def pybullet_ant():
  # General
  algorithm = agents.ppo.PPOAlgorithm
  num_agents = 10
  eval_episodes = 25
  use_gpu = False
  # Environment
  env = 'AntBulletEnv-v0'
  max_length = 1000
  steps = 1e7  # 10M
  # Network
  network = agents.scripts.networks.feed_forward_gaussian
  weight_summaries = dict(
      all=r'.*',
      policy=r'.*/policy/.*',
      value=r'.*/value/.*')
  policy_layers = 200, 100
  value_layers = 200, 100
  init_mean_factor = 0.1
  init_logstd = -1
  # Optimization
  update_every = 30
  update_epochs = 25
  optimizer = tf.train.AdamOptimizer
  learning_rate = 1e-4
  # Losses
  discount = 0.995
  kl_target = 1e-2
  kl_cutoff_factor = 2
  kl_cutoff_coef = 1000
  kl_init_penalty = 1
  return locals()


def main(args):
  agents.scripts.utility.set_up_logging()
  logdir = args.logdir and os.path.expanduser(args.logdir)
  if logdir:
    logdir = os.path.join(logdir, '{}-{}'.format(args.timestamp, args.config))
  if args.mode == 'train':
    try:
      # Try to resume training.
      config = agents.scripts.utility.load_config(args.logdir)
    except IOError:
      # Start new training run.
      config = agents.tools.AttrDict(globals()[args.config]())
      config = agents.scripts.utility.save_config(config, logdir)
    for score in agents.scripts.train.train(config, env_processes=True):
      logging.info('Score {}.'.format(score))
  if args.mode == 'render':
    agents.scripts.visualize.visualize(
        logdir=args.logdir, outdir=args.logdir, num_agents=1, num_episodes=5,
        checkpoint=None, env_processes=True)


if __name__ == '__main__':
  timestamp = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')
  parser = argparse.ArgumentParser()
  parser.add_argument('--mode', choices=['train', 'render'], default='train')
  parser.add_argument('--logdir', default='~/logdir/varagent')
  parser.add_argument('--config')
  parser.add_argument('--timestamp', default=timestamp)
  args = parser.parse_args()
  main(args)