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2b5d1dabe6
* 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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778 lines
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<div class="section" id="test">
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<h1>test<a class="headerlink" href="#test" title="Permalink to this headline">¶</a></h1>
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<div class="admonition important">
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<p class="first admonition-title">Important</p>
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<p class="last">Its a note! in markdown!</p>
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</div>
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<dl class="class">
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<dt id="rl_coach.agents.dqn_agent.DQNAgent">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.dqn_agent.</code><code class="descname">DQNAgent</code><span class="sig-paren">(</span><em>agent_parameters</em>, <em>parent: Union[LevelManager</em>, <em>CompositeAgent] = None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rl_coach/agents/dqn_agent.html#DQNAgent"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent" title="Permalink to this definition">¶</a></dt>
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<dd><dl class="method">
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<dt id="rl_coach.agents.dqn_agent.DQNAgent.act">
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<code class="descname">act</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → rl_coach.core_types.ActionInfo<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.act" title="Permalink to this definition">¶</a></dt>
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<dd><p>Given the agents current knowledge, decide on the next action to apply to the environment</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">An ActionInfo object, which contains the action and any additional info from the action decision process</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="rl_coach.agents.dqn_agent.DQNAgent.call_memory">
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<code class="descname">call_memory</code><span class="sig-paren">(</span><em>func</em>, <em>args=()</em><span class="sig-paren">)</span><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.call_memory" title="Permalink to this definition">¶</a></dt>
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<dd><p>This function is a wrapper to allow having the same calls for shared or unshared memories.
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It should be used instead of calling the memory directly in order to allow different algorithms to work
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both with a shared and a local memory.</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>func</strong> – the name of the memory function to call</li>
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<li><strong>args</strong> – the arguments to supply to the function</li>
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</ul>
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</td>
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</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the return value of the function</p>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="rl_coach.agents.dqn_agent.DQNAgent.choose_action">
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<code class="descname">choose_action</code><span class="sig-paren">(</span><em>curr_state</em><span class="sig-paren">)</span><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.choose_action" title="Permalink to this definition">¶</a></dt>
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<dd><p>choose an action to act with in the current episode being played. Different behavior might be exhibited when
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training or testing.</p>
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<table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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||
<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>curr_state</strong> – the current state to act upon.</td>
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</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">chosen action, some action value describing the action (q-value, probability, etc)</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="rl_coach.agents.dqn_agent.DQNAgent.collect_savers">
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<code class="descname">collect_savers</code><span class="sig-paren">(</span><em>parent_path_suffix: str</em><span class="sig-paren">)</span> → rl_coach.saver.SaverCollection<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.collect_savers" title="Permalink to this definition">¶</a></dt>
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<dd><p>Collect all of agent’s network savers
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:param parent_path_suffix: path suffix of the parent of the agent
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(could be name of level manager or composite agent)
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:return: collection of all agent savers</p>
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</dd></dl>
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<dl class="method">
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<dt id="rl_coach.agents.dqn_agent.DQNAgent.create_networks">
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<code class="descname">create_networks</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Dict[str, rl_coach.architectures.network_wrapper.NetworkWrapper]<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.create_networks" title="Permalink to this definition">¶</a></dt>
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<dd><p>Create all the networks of the agent.
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The network creation will be done after setting the environment parameters for the agent, since they are needed
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for creating the network.</p>
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<table class="docutils field-list" frame="void" rules="none">
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||
<col class="field-name" />
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||
<col class="field-body" />
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||
<tbody valign="top">
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||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">A list containing all the networks</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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<dl class="method">
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<dt id="rl_coach.agents.dqn_agent.DQNAgent.emulate_act_on_trainer">
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<code class="descname">emulate_act_on_trainer</code><span class="sig-paren">(</span><em>transition: rl_coach.core_types.Transition</em><span class="sig-paren">)</span> → rl_coach.core_types.ActionInfo<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.emulate_act_on_trainer" title="Permalink to this definition">¶</a></dt>
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<dd><p>This emulates the act using the transition obtained from the rollout worker on the training worker
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in case of distributed training.
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Given the agents current knowledge, decide on the next action to apply to the environment
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||
:return: an action and a dictionary containing any additional info from the action decision process</p>
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</dd></dl>
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<dl class="method">
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<dt id="rl_coach.agents.dqn_agent.DQNAgent.emulate_observe_on_trainer">
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<code class="descname">emulate_observe_on_trainer</code><span class="sig-paren">(</span><em>transition: rl_coach.core_types.Transition</em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.emulate_observe_on_trainer" title="Permalink to this definition">¶</a></dt>
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<dd><p>This emulates the observe using the transition obtained from the rollout worker on the training worker
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in case of distributed training.
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Given a response from the environment, distill the observation from it and store it for later use.
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The response should be a dictionary containing the performed action, the new observation and measurements,
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the reward, a game over flag and any additional information necessary.
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:return:</p>
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</dd></dl>
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<dl class="method">
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<dt id="rl_coach.agents.dqn_agent.DQNAgent.get_predictions">
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<code class="descname">get_predictions</code><span class="sig-paren">(</span><em>states: List[Dict[str, numpy.ndarray]], prediction_type: rl_coach.core_types.PredictionType</em><span class="sig-paren">)</span><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.get_predictions" title="Permalink to this definition">¶</a></dt>
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<dd><p>Get a prediction from the agent with regard to the requested prediction_type.
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If the agent cannot predict this type of prediction_type, or if there is more than possible way to do so,
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raise a ValueException.</p>
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||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
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||
<col class="field-body" />
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||
<tbody valign="top">
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||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
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<li><strong>states</strong> – The states to get a prediction for</li>
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<li><strong>prediction_type</strong> – The type of prediction to get for the states. For example, the state-value prediction.</li>
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</ul>
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</td>
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</tr>
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<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the predicted values</p>
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||
</td>
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||
</tr>
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||
</tbody>
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</table>
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||
</dd></dl>
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||
|
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<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.get_state_embedding">
|
||
<code class="descname">get_state_embedding</code><span class="sig-paren">(</span><em>state: dict</em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.get_state_embedding" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Given a state, get the corresponding state embedding from the main network</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
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||
<col class="field-body" />
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||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>state</strong> – a state dict</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">a numpy embedding vector</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.handle_episode_ended">
|
||
<code class="descname">handle_episode_ended</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.handle_episode_ended" title="Permalink to this definition">¶</a></dt>
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||
<dd><p>Make any changes needed when each episode is ended.
|
||
This includes incrementing counters, updating full episode dependent values, updating logs, etc.
|
||
This function is called right after each episode is ended.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.init_environment_dependent_modules">
|
||
<code class="descname">init_environment_dependent_modules</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.init_environment_dependent_modules" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Initialize any modules that depend on knowing information about the environment such as the action space or
|
||
the observation space</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
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||
<col class="field-body" />
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||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
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||
</tr>
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||
</tbody>
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||
</table>
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||
</dd></dl>
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||
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||
<dl class="method">
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||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.learn_from_batch">
|
||
<code class="descname">learn_from_batch</code><span class="sig-paren">(</span><em>batch</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/rl_coach/agents/dqn_agent.html#DQNAgent.learn_from_batch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.learn_from_batch" title="Permalink to this definition">¶</a></dt>
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||
<dd><p>Given a batch of transitions, calculates their target values and updates the network.</p>
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||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
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||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>batch</strong> – A list of transitions</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">The total loss of the training, the loss per head and the unclipped gradients</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.log_to_screen">
|
||
<code class="descname">log_to_screen</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.log_to_screen" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Write an episode summary line to the terminal</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.observe">
|
||
<code class="descname">observe</code><span class="sig-paren">(</span><em>env_response: rl_coach.core_types.EnvResponse</em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.observe" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Given a response from the environment, distill the observation from it and store it for later use.
|
||
The response should be a dictionary containing the performed action, the new observation and measurements,
|
||
the reward, a game over flag and any additional information necessary.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>env_response</strong> – result of call from environment.step(action)</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">a boolean value which determines if the agent has decided to terminate the episode after seeing the
|
||
given observation</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="attribute">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.parent">
|
||
<code class="descname">parent</code><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.parent" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Get the parent class of the agent</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">the current phase</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="attribute">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.phase">
|
||
<code class="descname">phase</code><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.phase" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>The current running phase of the agent</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">RunPhase</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.post_training_commands">
|
||
<code class="descname">post_training_commands</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.post_training_commands" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>A function which allows adding any functionality that is required to run right after the training phase ends.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.prepare_batch_for_inference">
|
||
<code class="descname">prepare_batch_for_inference</code><span class="sig-paren">(</span><em>states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str</em><span class="sig-paren">)</span> → Dict[str, numpy.core.multiarray.array]<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.prepare_batch_for_inference" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Convert curr_state into input tensors tensorflow is expecting. i.e. if we have several inputs states, stack all
|
||
observations together, measurements together, etc.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||
<li><strong>states</strong> – A list of environment states, where each one is a dict mapping from an observation name to its
|
||
corresponding observation</li>
|
||
<li><strong>network_name</strong> – The agent network name to prepare the batch for. this is needed in order to extract only
|
||
the observation relevant for the network from the states.</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A dictionary containing a list of values from all the given states for each of the observations</p>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.register_signal">
|
||
<code class="descname">register_signal</code><span class="sig-paren">(</span><em>signal_name: str</em>, <em>dump_one_value_per_episode: bool = True</em>, <em>dump_one_value_per_step: bool = False</em><span class="sig-paren">)</span> → rl_coach.utils.Signal<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.register_signal" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Register a signal such that its statistics will be dumped and be viewable through dashboard</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||
<li><strong>signal_name</strong> – the name of the signal as it will appear in dashboard</li>
|
||
<li><strong>dump_one_value_per_episode</strong> – should the signal value be written for each episode?</li>
|
||
<li><strong>dump_one_value_per_step</strong> – should the signal value be written for each step?</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the created signal</p>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.reset_evaluation_state">
|
||
<code class="descname">reset_evaluation_state</code><span class="sig-paren">(</span><em>val: rl_coach.core_types.RunPhase</em><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.reset_evaluation_state" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Perform accumulators initialization when entering an evaluation phase, and signal dumping when exiting an
|
||
evaluation phase. Entering or exiting the evaluation phase is determined according to the new phase given
|
||
by val, and by the current phase set in self.phase.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>val</strong> – The new phase to change to</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.reset_internal_state">
|
||
<code class="descname">reset_internal_state</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.reset_internal_state" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Reset all the episodic parameters. This function is called right before each episode starts.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.restore_checkpoint">
|
||
<code class="descname">restore_checkpoint</code><span class="sig-paren">(</span><em>checkpoint_dir: str</em><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.restore_checkpoint" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Allows agents to store additional information when saving checkpoints.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>checkpoint_dir</strong> – The checkpoint dir to restore from</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.run_pre_network_filter_for_inference">
|
||
<code class="descname">run_pre_network_filter_for_inference</code><span class="sig-paren">(</span><em>state: Dict[str, numpy.ndarray], update_filter_internal_state: bool = True</em><span class="sig-paren">)</span> → Dict[str, numpy.ndarray]<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.run_pre_network_filter_for_inference" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Run filters which where defined for being applied right before using the state for inference.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||
<li><strong>state</strong> – The state to run the filters on</li>
|
||
<li><strong>update_filter_internal_state</strong> – Should update the filter’s internal state - should not update when evaluating</li>
|
||
</ul>
|
||
</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">The filtered state</p>
|
||
</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.save_checkpoint">
|
||
<code class="descname">save_checkpoint</code><span class="sig-paren">(</span><em>checkpoint_prefix: str</em><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.save_checkpoint" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Allows agents to store additional information when saving checkpoints.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>checkpoint_prefix</strong> – The prefix of the checkpoint file to save</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.set_environment_parameters">
|
||
<code class="descname">set_environment_parameters</code><span class="sig-paren">(</span><em>spaces: rl_coach.spaces.SpacesDefinition</em><span class="sig-paren">)</span><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.set_environment_parameters" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Sets the parameters that are environment dependent. As a side effect, initializes all the components that are
|
||
dependent on those values, by calling init_environment_dependent_modules</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>spaces</strong> – the environment spaces definition</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.set_incoming_directive">
|
||
<code class="descname">set_incoming_directive</code><span class="sig-paren">(</span><em>action: Union[int, float, numpy.ndarray, List]</em><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.set_incoming_directive" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Allows setting a directive for the agent to follow. This is useful in hierarchy structures, where the agent
|
||
has another master agent that is controlling it. In such cases, the master agent can define the goals for the
|
||
slave agent, define it’s observation, possible actions, etc. The directive type is defined by the agent
|
||
in-action-space.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>action</strong> – The action that should be set as the directive</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.set_session">
|
||
<code class="descname">set_session</code><span class="sig-paren">(</span><em>sess</em><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.set_session" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Set the deep learning framework session for all the agents in the composite agent</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.setup_logger">
|
||
<code class="descname">setup_logger</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.setup_logger" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Setup the logger for the agent</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.sync">
|
||
<code class="descname">sync</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.sync" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Sync the global network parameters to local networks</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.train">
|
||
<code class="descname">train</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → float<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.train" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Check if a training phase should be done as configured by num_consecutive_playing_steps.
|
||
If it should, then do several training steps as configured by num_consecutive_training_steps.
|
||
A single training iteration: Sample a batch, train on it and update target networks.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">The total training loss during the training iterations.</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.update_log">
|
||
<code class="descname">update_log</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.update_log" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Updates the episodic log file with all the signal values from the most recent episode.
|
||
Additional signals for logging can be set by the creating a new signal using self.register_signal,
|
||
and then updating it with some internal agent values.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.update_step_in_episode_log">
|
||
<code class="descname">update_step_in_episode_log</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.update_step_in_episode_log" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Updates the in-episode log file with all the signal values from the most recent step.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">None</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
<dl class="method">
|
||
<dt id="rl_coach.agents.dqn_agent.DQNAgent.update_transition_before_adding_to_replay_buffer">
|
||
<code class="descname">update_transition_before_adding_to_replay_buffer</code><span class="sig-paren">(</span><em>transition: rl_coach.core_types.Transition</em><span class="sig-paren">)</span> → rl_coach.core_types.Transition<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.update_transition_before_adding_to_replay_buffer" title="Permalink to this definition">¶</a></dt>
|
||
<dd><p>Allows agents to update the transition just before adding it to the replay buffer.
|
||
Can be useful for agents that want to tweak the reward, termination signal, etc.</p>
|
||
<table class="docutils field-list" frame="void" rules="none">
|
||
<col class="field-name" />
|
||
<col class="field-body" />
|
||
<tbody valign="top">
|
||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>transition</strong> – the transition to update</td>
|
||
</tr>
|
||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">the updated transition</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</dd></dl>
|
||
|
||
</dd></dl>
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