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<a href="https://github.com/ALRhub/fancy_gym/blob/release/docs/source/guide/episodic_rl.rst" class="fa fa-github"> Edit on GitHub</a>
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<section id="what-is-episodic-rl">
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<h1>What is Episodic RL?<a class="headerlink" href="#what-is-episodic-rl" title="Permalink to this heading"></a></h1>
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<div class="justify"><p>Movement primitive (MP) environments differ from traditional step-based
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environments. They align more with concepts from stochastic search,
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black-box optimization, and methods commonly found in classical robotics
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and control. Instead of individual steps, MP environments operate on an
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episode basis, executing complete trajectories. These trajectories are
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produced by trajectory generators like Dynamic Movement Primitives
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(DMP), Probabilistic Movement Primitives (ProMP) or Probabilistic
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Dynamic Movement Primitives (ProDMP).</p>
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<p>Once generated, these trajectories are converted into step-by-step
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actions using a trajectory tracking controller. The specific controller
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chosen depends on the environment’s requirements. Currently, we support
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position, velocity, and PD-Controllers tailored for position, velocity,
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and torque control. Additionally, we have a specialized controller
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designed for the MetaWorld control suite.</p>
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<p>While the overarching objective of MP environments remains the learning
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of an optimal policy, the actions here represent the parametrization of
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motion primitives to craft the right trajectory. Our framework further
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enhances this by accommodating a contextual setting. At the episode’s
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onset, we present the context space—a subset of the observation space.
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This demands the prediction of a new action or MP parametrization for
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every unique context.</p>
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<p>© Copyright 2020-2024, Fabian Otto, Onur Celik, Dominik Roth, Hongyi Zhou.</p>
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