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[containers] Enables container support for scenes that use visual observations
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‎Dockerfile‎

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# Use an official Python runtime as a parent image
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FROM python:3.6-slim
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# Based off of python:3.6-slim, except that we are using ubuntu instead of debian.
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FROM ubuntu:16.04
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# ensure local python is preferred over distribution python
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ENV PATH /usr/local/bin:$PATH
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# http://bugs.python.org/issue19846
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# > At the moment, setting "LANG=C" on a Linux system *fundamentally breaks Python 3*, and that's not OK.
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ENV LANG C.UTF-8
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# runtime dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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ca-certificates \
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libexpat1 \
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libffi6 \
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libgdbm3 \
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libreadline6 \
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libsqlite3-0 \
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libssl1.0.0 \
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&& rm -rf /var/lib/apt/lists/*
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ENV GPG_KEY 0D96DF4D4110E5C43FBFB17F2D347EA6AA65421D
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ENV PYTHON_VERSION 3.6.4
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RUN set -ex \
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&& buildDeps=" \
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dpkg-dev \
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gcc \
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libbz2-dev \
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libc6-dev \
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libexpat1-dev \
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libffi-dev \
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libgdbm-dev \
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liblzma-dev \
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libncursesw5-dev \
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libreadline-dev \
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libsqlite3-dev \
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libssl-dev \
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make \
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tcl-dev \
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tk-dev \
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wget \
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xz-utils \
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zlib1g-dev \
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# as of Stretch, "gpg" is no longer included by default
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$(command -v gpg > /dev/null || echo 'gnupg dirmngr') \
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" \
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&& apt-get update && apt-get install -y $buildDeps --no-install-recommends && rm -rf /var/lib/apt/lists/* \
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\
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&& wget -O python.tar.xz "https://www.python.org/ftp/python/${PYTHON_VERSION%%[a-z]*}/Python-$PYTHON_VERSION.tar.xz" \
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&& wget -O python.tar.xz.asc "https://www.python.org/ftp/python/${PYTHON_VERSION%%[a-z]*}/Python-$PYTHON_VERSION.tar.xz.asc" \
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&& export GNUPGHOME="$(mktemp -d)" \
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&& gpg --keyserver ha.pool.sks-keyservers.net --recv-keys "$GPG_KEY" \
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&& gpg --batch --verify python.tar.xz.asc python.tar.xz \
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&& rm -rf "$GNUPGHOME" python.tar.xz.asc \
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&& mkdir -p /usr/src/python \
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&& tar -xJC /usr/src/python --strip-components=1 -f python.tar.xz \
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&& rm python.tar.xz \
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\
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&& cd /usr/src/python \
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&& gnuArch="$(dpkg-architecture --query DEB_BUILD_GNU_TYPE)" \
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&& ./configure \
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--build="$gnuArch" \
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--enable-loadable-sqlite-extensions \
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--enable-shared \
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--with-system-expat \
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--with-system-ffi \
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--without-ensurepip \
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&& make -j "$(nproc)" \
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&& make install \
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&& ldconfig \
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\
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&& apt-get purge -y --auto-remove $buildDeps \
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\
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&& find /usr/local -depth \
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\( \
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\( -type d -a \( -name test -o -name tests \) \) \
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-o \
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\( -type f -a \( -name '*.pyc' -o -name '*.pyo' \) \) \
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\) -exec rm -rf '{}' + \
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&& rm -rf /usr/src/python
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# make some useful symlinks that are expected to exist
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RUN cd /usr/local/bin \
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&& ln -s idle3 idle \
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&& ln -s pydoc3 pydoc \
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&& ln -s python3 python \
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&& ln -s python3-config python-config
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# if this is called "PIP_VERSION", pip explodes with "ValueError: invalid truth value '<VERSION>'"
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ENV PYTHON_PIP_VERSION 9.0.3
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RUN set -ex; \
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\
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apt-get update; \
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apt-get install -y --no-install-recommends wget; \
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rm -rf /var/lib/apt/lists/*; \
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\
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wget -O get-pip.py 'https://bootstrap.pypa.io/get-pip.py'; \
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\
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apt-get purge -y --auto-remove wget; \
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\
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python get-pip.py \
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--disable-pip-version-check \
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--no-cache-dir \
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"pip==$PYTHON_PIP_VERSION" \
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; \
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pip --version; \
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\
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find /usr/local -depth \
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\( \
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\( -type d -a \( -name test -o -name tests \) \) \
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-o \
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\( -type f -a \( -name '*.pyc' -o -name '*.pyo' \) \) \
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\) -exec rm -rf '{}' +; \
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rm -f get-pip.py
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CMD ["python3"]
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RUN apt-get update && apt-get -y upgrade
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# xvfb is used to do CPU based rendering of Unity
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RUN apt-get install -y xvfb
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ADD python/requirements.txt .
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RUN pip install --trusted-host pypi.python.org -r requirements.txt
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‎docs/Using-Docker.md‎

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# Using Docker For ML-Agents (Experimental)
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We currently offer an experimental solution for Windows and Mac users who would like to do training or inference using Docker. This option may be appealing to those who would like to avoid installing Python and TensorFlow themselves. The current setup forces both TensorFlow and Unity to _only_ rely on the CPU for computations. Consequently, our Docker support is limited to environments whose agents **do not** use camera-based visual observations. For example, the [GridWorld](Learning-Environment-Examples.md#gridworld) environment is **not** supported.
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We currently offer a solution for Windows and Mac users who would like to do training or inference using Docker. This option may be appealing to those who would like to avoid installing Python and TensorFlow themselves. The current setup forces both TensorFlow and Unity to _only_ rely on the CPU for computations. Consequently, our Docker simulation [uses the CPU](https://en.wikipedia.org/wiki/Xvfb) to do visual rendering. This means that environments which involve agents using camera-based visual observations might be slower.
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## Requirements
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- Unity _Linux Build Support_ Component
@@ -26,8 +27,6 @@ Since Docker typically runs a container sharing a (linux) kernel with the host m
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Unity environment **has** to be built for the **linux platform**. When building a Unity environment, please select the following options from the the Build Settings window:
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- Set the _Target Platform_ to `Linux`
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- Set the _Architecture_ to `x86_64`
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- `Uncheck` the _Development Build_ option
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- `Check` the _Headless Mode_ option. (_This is required because the Unity binary will run in a container that does not have graphics drivers installed_.)
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![Build Settings For Docker](images/docker_build_settings.png)
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### Stopping Container and Saving State
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If you are satisfied with the training progress, you can stop the Docker container while saving state using the following command:
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If you are satisfied with the training progress, you can stop the Docker container while saving state by either using `Ctrl+C` or `⌘+C` (Mac) or by using the following command:
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```
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docker kill --signal=SIGINT <container-name>

‎docs/images/docker_build_settings.png‎

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‎python/unityagents/environment.py‎

Lines changed: 17 additions & 6 deletions
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@@ -23,7 +23,7 @@
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class UnityEnvironment(object):
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def __init__(self, file_name, worker_id=0,
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base_port=5005, curriculum=None,
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seed=0):
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seed=0, docker_training=False):
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"""
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Starts a new unity environment and establishes a connection with the environment.
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Notice: Currently communication between Unity and Python takes place over an open socket without authentication.
@@ -32,6 +32,7 @@ def __init__(self, file_name, worker_id=0,
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:string file_name: Name of Unity environment binary.
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:int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
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:int worker_id: Number to add to communication port (5005) [0]. Used for asynchronous agent scenarios.
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:param docker_training: Informs this class whether the process is being run within a container.
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"""
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atexit.register(self.close)
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else:
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logger.debug("This is the launch string {}".format(launch_string))
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# Launch Unity environment
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proc1 = subprocess.Popen(
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[launch_string,
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'--port', str(self.port),
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'--seed', str(seed)])
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if docker_training == False:
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proc1 = subprocess.Popen(
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[launch_string,
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'--port', str(self.port),
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'--seed', str(seed)])
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else:
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docker_ls = ("exec xvfb-run --auto-servernum"
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" --server-args='-screen 0 640x480x24'"
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" {0} --port {1} --seed {2}").format(launch_string,
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str(self.port),
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str(seed))
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proc1 = subprocess.Popen(docker_ls,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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shell=True)
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self._socket.settimeout(30)
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try:
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try:

‎python/unitytrainers/ppo/trainer.py‎

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@@ -214,7 +214,6 @@ def add_experiences(self, curr_all_info: AllBrainInfo, next_all_info: AllBrainIn
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:param next_all_info: Dictionary of all current brains and corresponding BrainInfo.
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:param take_action_outputs: The outputs of the take action method.
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"""
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curr_info = curr_all_info[self.brain_name]
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next_info = next_all_info[self.brain_name]
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‎python/unitytrainers/trainer_controller.py‎

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Original file line numberDiff line numberDiff line change
@@ -42,10 +42,12 @@ def __init__(self, env_path, run_id, save_freq, curriculum_file, fast_simulation
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.replace('.x86', '')) # Strip out executable extensions if passed
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# Recognize and use docker volume if one is passed as an argument
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if docker_target_name == '':
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self.docker_training = False
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self.model_path = './models/{run_id}'.format(run_id=run_id)
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self.curriculum_file = curriculum_file
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self.summaries_dir = './summaries'
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else:
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self.docker_training = True
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self.model_path = '/{docker_target_name}/models/{run_id}'.format(
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docker_target_name=docker_target_name,
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run_id=run_id)
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np.random.seed(self.seed)
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tf.set_random_seed(self.seed)
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self.env = UnityEnvironment(file_name=env_path, worker_id=self.worker_id,
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curriculum=self.curriculum_file, seed=self.seed)
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curriculum=self.curriculum_file, seed=self.seed,
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docker_training=self.docker_training)
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self.env_name = os.path.basename(os.path.normpath(env_path)) # Extract out name of environment
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def _get_progress(self):

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