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* initial ACER commit * Code cleanup + several fixes * Q-retrace bug fix + small clean-ups * added documentation for acer * ACER benchmarks * update benchmarks table * Add nightly running of golden and trace tests. (#202) Resolves #200 * comment out nightly trace tests until values reset. * remove redundant observe ignore (#168) * ensure nightly test env containers exist. (#205) Also bump integration test timeout * wxPython removal (#207) Replacing wxPython with Python's Tkinter. Also removing the option to choose multiple files as it is unused and causes errors, and fixing the load file/directory spinner. * Create CONTRIBUTING.md (#210) * Create CONTRIBUTING.md. Resolves #188 * run nightly golden tests sequentially. (#217) Should reduce resource requirements and potential CPU contention but increases overall execution time. * tests: added new setup configuration + test args (#211) - added utils for future tests and conftest - added test args * new docs build * golden test update
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<p class="caption"><span class="caption-text">Intro</span></p>
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<div class="section" id="usage">
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<h1>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">¶</a></h1>
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<p>One of the mechanisms Coach uses for running experiments is the <strong>Preset</strong> mechanism.
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As its name implies, a preset defines a set of predefined experiment parameters.
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This allows defining a <em>complex</em> agent-environment interaction, with multiple parameters, and later running it through
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a very <em>simple</em> command line.</p>
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<p>The preset includes all the components that are used in the experiment, such as the agent internal components and
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the environment to use.
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It additionally defines general parameters for the experiment itself, such as the training schedule,
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visualization parameters, and testing parameters.</p>
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<div class="section" id="training-an-agent">
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<h2>Training an Agent<a class="headerlink" href="#training-an-agent" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="single-threaded-algorithms">
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<h3>Single-threaded Algorithms<a class="headerlink" href="#single-threaded-algorithms" title="Permalink to this headline">¶</a></h3>
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<p>This is the most common case. Just choose a preset using the <cite>-p</cite> flag and press enter.
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To list the available presets, use the <cite>-l</cite> flag.</p>
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<p><em>Example:</em></p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">p</span> <span class="n">CartPole_DQN</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="multi-threaded-algorithms">
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<h3>Multi-threaded Algorithms<a class="headerlink" href="#multi-threaded-algorithms" title="Permalink to this headline">¶</a></h3>
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<p>Multi-threaded algorithms are very common these days.
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They typically achieve the best results, and scale gracefully with the number of threads.
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In Coach, running such algorithms is done by selecting a suitable preset, and choosing the number of threads to run using the <code class="code docutils literal notranslate"><span class="pre">-n</span></code> flag.</p>
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<p><em>Example:</em></p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">p</span> <span class="n">CartPole_A3C</span> <span class="o">-</span><span class="n">n</span> <span class="mi">8</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="multi-node-algorithms">
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<h3>Multi-Node Algorithms<a class="headerlink" href="#multi-node-algorithms" title="Permalink to this headline">¶</a></h3>
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<p>Coach supports the multi-node runs in distributed mode. Specifically, the horizontal scale-out of rollout workers is implemented.
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In Coach, running such algorithms is done by selecting a suitable preset, enabling distributed coach using <code class="code docutils literal notranslate"><span class="pre">-dc</span></code> flag,
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passing distributed coach parameters using <code class="code docutils literal notranslate"><span class="pre">dcp</span></code> and choosing the number of to run using the <code class="code docutils literal notranslate"><span class="pre">-n</span></code> flag.
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For more details and instructions on how to use distributed Coach, see <a class="reference internal" href="dist_usage.html#dist-coach-usage"><span class="std std-ref">Usage - Distributed Coach</span></a>.</p>
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<p><em>Example:</em></p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">p</span> <span class="n">CartPole_ClippedPPO</span> <span class="o">-</span><span class="n">dc</span> <span class="o">-</span><span class="n">dcp</span> <span class="o"><</span><span class="n">path</span><span class="o">-</span><span class="n">to</span><span class="o">-</span><span class="n">config</span><span class="o">-</span><span class="nb">file</span><span class="o">></span> <span class="o">-</span><span class="n">n</span> <span class="mi">8</span>
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</pre></div>
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</div>
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</div>
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</div>
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<div class="section" id="evaluating-an-agent">
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<h2>Evaluating an Agent<a class="headerlink" href="#evaluating-an-agent" title="Permalink to this headline">¶</a></h2>
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<p>There are several options for evaluating an agent during the training:</p>
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<ul class="simple">
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<li>For multi-threaded runs, an evaluation agent will constantly run in the background and evaluate the model during the training.</li>
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<li>For single-threaded runs, it is possible to define an evaluation period through the preset. This will run several episodes of evaluation once in a while.</li>
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</ul>
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<p>Additionally, it is possible to save checkpoints of the agents networks and then run only in evaluation mode.
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Saving checkpoints can be done by specifying the number of seconds between storing checkpoints using the <code class="code docutils literal notranslate"><span class="pre">-s</span></code> flag.
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The checkpoints will be saved into the experiment directory.
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Loading a model for evaluation can be done by specifying the <code class="code docutils literal notranslate"><span class="pre">-crd</span></code> flag with the experiment directory, and the <code class="code docutils literal notranslate"><span class="pre">--evaluate</span></code> flag to disable training.</p>
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<p><em>Example:</em></p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">p</span> <span class="n">CartPole_DQN</span> <span class="o">-</span><span class="n">s</span> <span class="mi">60</span>
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<span class="n">coach</span> <span class="o">-</span><span class="n">p</span> <span class="n">CartPole_DQN</span> <span class="o">--</span><span class="n">evaluate</span> <span class="o">-</span><span class="n">crd</span> <span class="n">CHECKPOINT_RESTORE_DIR</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="playing-with-the-environment-as-a-human">
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<h2>Playing with the Environment as a Human<a class="headerlink" href="#playing-with-the-environment-as-a-human" title="Permalink to this headline">¶</a></h2>
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<p>Interacting with the environment as a human can be useful for understanding its difficulties and for collecting data for imitation learning.
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In Coach, this can be easily done by selecting a preset that defines the environment to use, and specifying the <code class="code docutils literal notranslate"><span class="pre">--play</span></code> flag.
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When the environment is loaded, the available keyboard buttons will be printed to the screen.
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Pressing the escape key when finished will end the simulation and store the replay buffer in the experiment dir.</p>
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<p><em>Example:</em></p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">et</span> <span class="n">rl_coach</span><span class="o">.</span><span class="n">environments</span><span class="o">.</span><span class="n">gym_environment</span><span class="p">:</span><span class="n">Atari</span> <span class="o">-</span><span class="n">lvl</span> <span class="n">BreakoutDeterministic</span><span class="o">-</span><span class="n">v4</span> <span class="o">--</span><span class="n">play</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="learning-through-imitation-learning">
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<h2>Learning Through Imitation Learning<a class="headerlink" href="#learning-through-imitation-learning" title="Permalink to this headline">¶</a></h2>
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<p>Learning through imitation of human behavior is a nice way to speedup the learning.
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In Coach, this can be done in two steps -</p>
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<ol class="arabic">
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<li><p class="first">Create a dataset of demonstrations by playing with the environment as a human.
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After this step, a pickle of the replay buffer containing your game play will be stored in the experiment directory.
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The path to this replay buffer will be printed to the screen.
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To do so, you should select an environment type and level through the command line, and specify the <code class="code docutils literal notranslate"><span class="pre">--play</span></code> flag.</p>
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<blockquote>
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<div><p><em>Example:</em></p>
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</div></blockquote>
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</li>
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</ol>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">et</span> <span class="n">rl_coach</span><span class="o">.</span><span class="n">environments</span><span class="o">.</span><span class="n">doom_environment</span><span class="p">:</span><span class="n">DoomEnvironmentParameters</span> <span class="o">-</span><span class="n">lvl</span> <span class="n">Basic</span> <span class="o">--</span><span class="n">play</span>
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</pre></div>
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</div>
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<ol class="arabic" start="2">
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<li><dl class="first docutils">
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<dt>Next, use an imitation learning preset and set the replay buffer path accordingly.</dt>
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<dd><p class="first">The path can be set either from the command line or from the preset itself.</p>
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<p class="last"><em>Example:</em></p>
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</dd>
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</dl>
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</li>
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</ol>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">p</span> <span class="n">Doom_Basic_BC</span> <span class="o">-</span><span class="n">cp</span><span class="o">=</span><span class="s1">'agent.load_memory_from_file_path=</span><span class="se">\"</span><span class="s1"><experiment dir>/replay_buffer.p</span><span class="se">\"</span><span class="s1">'</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="visualizations">
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<h2>Visualizations<a class="headerlink" href="#visualizations" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="rendering-the-environment">
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<h3>Rendering the Environment<a class="headerlink" href="#rendering-the-environment" title="Permalink to this headline">¶</a></h3>
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<p>Rendering the environment can be done by using the <code class="code docutils literal notranslate"><span class="pre">-r</span></code> flag.
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When working with multi-threaded algorithms, the rendered image will be representing the game play of the evaluation worker.
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When working with single-threaded algorithms, the rendered image will be representing the single worker which can be either training or evaluating.
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Keep in mind that rendering the environment in single-threaded algorithms may slow the training to some extent.
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When playing with the environment using the <code class="code docutils literal notranslate"><span class="pre">--play</span></code> flag, the environment will be rendered automatically without the need for specifying the <code class="code docutils literal notranslate"><span class="pre">-r</span></code> flag.</p>
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<p><em>Example:</em></p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">p</span> <span class="n">Atari_DQN</span> <span class="o">-</span><span class="n">lvl</span> <span class="n">breakout</span> <span class="o">-</span><span class="n">r</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="dumping-gifs">
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<h3>Dumping GIFs<a class="headerlink" href="#dumping-gifs" title="Permalink to this headline">¶</a></h3>
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<p>Coach allows storing GIFs of the agent game play.
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To dump GIF files, use the <code class="code docutils literal notranslate"><span class="pre">-dg</span></code> flag.
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The files are dumped after every evaluation episode, and are saved into the experiment directory, under a gifs sub-directory.</p>
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<p><em>Example:</em></p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">p</span> <span class="n">Atari_A3C</span> <span class="o">-</span><span class="n">lvl</span> <span class="n">breakout</span> <span class="o">-</span><span class="n">n</span> <span class="mi">4</span> <span class="o">-</span><span class="n">dg</span>
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</pre></div>
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</div>
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</div>
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</div>
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<div class="section" id="switching-between-deep-learning-frameworks">
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<h2>Switching Between Deep Learning Frameworks<a class="headerlink" href="#switching-between-deep-learning-frameworks" title="Permalink to this headline">¶</a></h2>
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<p>Coach uses TensorFlow as its main backend framework, but it also supports MXNet.
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MXNet is optional, and by default, TensorFlow will be used.
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If MXNet was installed, it is possible to switch to MXNet using the <code class="code docutils literal notranslate"><span class="pre">-f</span></code> flag.</p>
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<p><em>Example:</em></p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">coach</span> <span class="o">-</span><span class="n">p</span> <span class="n">Doom_Basic_DQN</span> <span class="o">-</span><span class="n">f</span> <span class="n">mxnet</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="additional-flags">
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<h2>Additional Flags<a class="headerlink" href="#additional-flags" title="Permalink to this headline">¶</a></h2>
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<p>There are several convenient flags which are important to know about.
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