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<div class="section" id="direct-future-prediction">
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<h1>Direct Future Prediction<a class="headerlink" href="#direct-future-prediction" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete</p>
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1611.01779">Learning to Act by Predicting the Future</a></p>
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<div class="section" id="network-structure">
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<h2>Network Structure<a class="headerlink" href="#network-structure" title="Permalink to this headline">¶</a></h2>
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<a class="reference internal image-reference" href="../../../_images/dfp.png"><img alt="../../../_images/dfp.png" class="align-center" src="../../../_images/dfp.png" style="width: 600px;" /></a>
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</div>
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<div class="section" id="algorithm-description">
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<h2>Algorithm Description<a class="headerlink" href="#algorithm-description" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="choosing-an-action">
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<h3>Choosing an action<a class="headerlink" href="#choosing-an-action" title="Permalink to this headline">¶</a></h3>
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<ol class="arabic simple">
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<li><p>The current states (observations and measurements) and the corresponding goal vector are passed as an input to the network.
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The output of the network is the predicted future measurements for time-steps <span class="math notranslate nohighlight">\(t+1,t+2,t+4,t+8,t+16\)</span> and
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<span class="math notranslate nohighlight">\(t+32\)</span> for each possible action.</p></li>
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<li><p>For each action, the measurements of each predicted time-step are multiplied by the goal vector,
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and the result is a single vector of future values for each action.</p></li>
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<li><p>Then, a weighted sum of the future values of each action is calculated, and the result is a single value for each action.</p></li>
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<li><p>The action values are passed to the exploration policy to decide on the action to use.</p></li>
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</ol>
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</div>
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<div class="section" id="training-the-network">
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<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
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<p>Given a batch of transitions, run them through the network to get the current predictions of the future measurements
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per action, and set them as the initial targets for training the network. For each transition
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<span class="math notranslate nohighlight">\((s_t,a_t,r_t,s_{t+1} )\)</span> in the batch, the target of the network for the action that was taken, is the actual
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measurements that were seen in time-steps <span class="math notranslate nohighlight">\(t+1,t+2,t+4,t+8,t+16\)</span> and <span class="math notranslate nohighlight">\(t+32\)</span>.
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For the actions that were not taken, the targets are the current values.</p>
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<dl class="class">
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<dt id="rl_coach.agents.dfp_agent.DFPAlgorithmParameters">
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<em class="property">class </em><code class="sig-prename descclassname">rl_coach.agents.dfp_agent.</code><code class="sig-name descname">DFPAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/dfp_agent.html#DFPAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.dfp_agent.DFPAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<dd><dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>num_predicted_steps_ahead</strong> – (int)
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Number of future steps to predict measurements for. The future steps won’t be sequential, but rather jump
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in multiples of 2. For example, if num_predicted_steps_ahead = 3, then the steps will be: t+1, t+2, t+4.
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The predicted steps will be [t + 2**i for i in range(num_predicted_steps_ahead)]</p></li>
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<li><p><strong>goal_vector</strong> – (List[float])
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The goal vector will weight each of the measurements to form an optimization goal. The vector should have
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the same length as the number of measurements, and it will be vector multiplied by the measurements.
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Positive values correspond to trying to maximize the particular measurement, and negative values
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correspond to trying to minimize the particular measurement.</p></li>
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<li><p><strong>future_measurements_weights</strong> – (List[float])
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The future_measurements_weights weight the contribution of each of the predicted timesteps to the optimization
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goal. For example, if there are 6 steps predicted ahead, and a future_measurements_weights vector with 3 values,
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then only the 3 last timesteps will be taken into account, according to the weights in the
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future_measurements_weights vector.</p></li>
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<li><p><strong>use_accumulated_reward_as_measurement</strong> – (bool)
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If set to True, the accumulated reward from the beginning of the episode will be added as a measurement to
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the measurements vector in the state. This van be useful in environments where the given measurements don’t
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include enough information for the particular goal the agent should achieve.</p></li>
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<li><p><strong>handling_targets_after_episode_end</strong> – (HandlingTargetsAfterEpisodeEnd)
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Dictates how to handle measurements that are outside the episode length.</p></li>
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<li><p><strong>scale_measurements_targets</strong> – (Dict[str, float])
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Allows rescaling the values of each of the measurements available. This van be useful when the measurements
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have a different scale and you want to normalize them to the same scale.</p></li>
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</ul>
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</dd>
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</dl>
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</dd></dl>
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