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.gitignore

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# Python
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__pycache__/
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*.py[cod]
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# Pip
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pip-selfcheck.json
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*.whl
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*.egg
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*.egg-info
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# Setuptools
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/build
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/dist
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*.eggs

AUTHORS

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# This is the list of Agents authors for copyright purposes.
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#
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# This does not necessarily list everyone who has contributed code, since in
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# some cases, their employer may be the copyright holder. To see the full list
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# of contributors, see the revision history in source control.
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Google Inc.
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Danijar Hafner

CONTRIBUTING.md

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# How to Contribute
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We'd love to accept your patches and contributions to this project. There are
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just a few small guidelines you need to follow.
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## Contributor License Agreement
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Contributions to this project must be accompanied by a Contributor License
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Agreement. You (or your employer) retain the copyright to your contribution,
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this simply gives us permission to use and redistribute your contributions as
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part of the project. Head over to <https://cla.developers.google.com/> to see
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your current agreements on file or to sign a new one.
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You generally only need to submit a CLA once, so if you've already submitted one
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(even if it was for a different project), you probably don't need to do it
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again.
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## Code reviews
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All submissions, including submissions by project members, require review. We
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use GitHub pull requests for this purpose. Consult
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[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
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information on using pull requests.

LICENSE

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Copyright 2017 The Agents Authors. All rights reserved.
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README.md

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<img src="https://www.tensorflow.org/images/tf_logo_transp.png" width=25% align="right">
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TensorFlow Agents
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=================
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This project provides optimized infrastructure for reinforcement learning. It
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extends the [OpenAI gym interface][post-gym] to multiple parallel environments
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and allows agents to be implemented in TensorFlow and perform batched
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computation. As a starting point, we provide BatchPPO, an optimized
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implementation of [Proximal Policy Optimization][post-ppo].
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Please cite the [white paper][paper-agents] if you use code from this project
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in your research:
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```bibtex
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@misc{hafner2017agents,
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title={TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow},
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author={Hafner, Danijar and Davidson, James and Vanhoucke, Vincent},
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year={2017}
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}
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```
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Dependencies: Python 2/3, TensorFlow 1.3+, Gym, rumamel.yaml
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[paper-agents]: https://drive.google.com/file/d/0B20Yn-GSaVHGMVlPanRTRlNIRlk/view
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[post-gym]: https://blog.openai.com/openai-gym-beta/
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[post-ppo]: https://blog.openai.com/openai-baselines-ppo/
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Instructions
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------------
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Clone the repository and run the PPO algorithm by typing:
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```shell
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python3 -m agents.scripts.train --logdir=/path/to/logdir --config=pendulum
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```
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The algorithm to use is defined in the configuration and `pendulum` started
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here uses the included PPO implementation. Check out more pre-defined
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configurations in `agents/scripts/configs.py`.
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If you want to resume a previously started run, add the `--timestamp=<time>`
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flag to the last command and provide the timestamp in the directory name of
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your run.
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To visualize metrics start TensorBoard from another terminal, then point your
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browser to `http://localhost:2222`:
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```shell
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tensorboard --logdir=/path/to/logdir --port=2222
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```
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To render videos and gather OpenAI Gym statistics to upload to the scoreboard,
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type:
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```shell
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python3 -m agents.scripts.visualize --logdir=/path/to/logdir/<time>-<config> --outdir=/path/to/outdir/
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```
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Modifications
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-------------
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We release this project as a starting point that makes it easy to implement new
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reinforcement learning ideas. These files are good places to start when
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modifying the code:
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| File | Content |
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| ---- | ------- |
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| `scripts/configs.py` | Experiment configurations specifying the tasks and algorithms. |
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| `scripts/networks.py` | Neural network models defined as [TensorFlow RNNCells][tf-rnn-cell]. |
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| `scripts/train.py` | The executable file containing the training setup. |
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| `ppo/algorithm.py` | The TensorFlow graph for the PPO algorithm. |
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To run all unit tests, type:
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```shell
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python3 -m unittest discover -p "*_test.py"
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```
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For further questions, please open an issue on Github.
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[tf-rnn-cell]: https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/RNNCell
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Implementation
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--------------
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We include a batched interface for OpenAI Gym environments that fully integrates
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with TensorFlow for efficient algorithm implementations. This is achieved
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through these core components:
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- **`agents.tools.wrappers.ExternalProcess`** is an environment wrapper that
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constructs an OpenAI Gym environment inside of an external process. Calls to
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`step()` and `reset()`, as well as attribute access, are forwarded to the
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process and wait for the result. This allows to run multiple environments in
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parallel without being restricted by Python's global interpreter lock.
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- **`agents.tools.BatchEnv`** extends the OpenAI Gym interface to batches of
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environments. It combines multiple OpenAI Gym environments, with `step()`
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accepting a batch of actions and returning a batch of observations, rewards,
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done flags, and info objects. If the individual environments live in external
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processes, they will be stepped in parallel.
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- **`agents.tools.InGraphBatchEnv`** integrates a batch environment into the
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TensorFlow graph and makes its `step()` and `reset()` functions accessible as
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operations. The current batch of observations, last actions, rewards, and done
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flags is stored in variables and made available as tensors.
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- **`agents.tools.simulate()`** fuses the step of an in-graph batch environment
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and a reinforcement learning algorithm together into a single operation to be
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called inside the training loop. This reduces the number of session calls and
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provieds a simple way to train future algorithms.
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To understand all the code, please make yourself familiar with TensorFlow's
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control flow operations, especially [`tf.cond()`][tf-cond],
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[`tf.scan()`][tf-scan], and
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[`tf.control_dependencies()`][tf-control-dependencies].
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[tf-cond]: https://www.tensorflow.org/api_docs/python/tf/cond
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[tf-scan]: https://www.tensorflow.org/api_docs/python/tf/scan
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[tf-control-dependencies]: https://www.tensorflow.org/api_docs/python/tf/control_dependencies
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Disclaimer
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----------
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This is not an official Google product.

agents/__init__.py

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# Copyright 2017 The TensorFlow Agents Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Main package of TensorFlow agents."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from . import ppo
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from . import scripts
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from . import tools

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