Motor skill learning with local trajectory methods

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Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review (2018, background): “This issue is discussed in more detail in Section 5.3.5 of (Levine, 2014).”
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood (2017, background): “It is tempting to group our approach with sequence learning methods which interpolate between supervised learning and reinforcement learning (Ranzato et al., 2015; Venkatraman et al., 2015; Ross et al., 2011; Norouzi et al., 2016; Bengio et al., 2015; Levine, 2014).”
Learning online alignments with continuous rewards policy gradient (2016, background): “Finally, we note that reinforcement learning models are often trained with augmented objectives that add an entropy penalty for actions are the too confident [18, 19].”
Cooperation and communication in multiagent deep reinforcement learning (2016, background): “Notable exceptions include Reinforcement Learning for Robots Using Neural Networks (Lin, 1992) and Motor Skill Learning with Local Trajectory Methods (Levine, 2014).”