Learning legged swimming gaits from experience

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Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (2017, background): “Model-based algorithms are known in general to outperform model-free learners in terms of sample complexity [4], and in practice have been applied successfully to control both simulated and real-world robotic systems, such as pendulums [6], legged robots [18], swimmers [19], and manipulators [20].”
Efficient reinforcement learning for robots using informative simulated priors (2015, background): “The hyperparmeters (σ2n, α 2, and Λ) are learned via evidence maximization [22] (lines 3 and 5 of Algorithm 1).”
Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning (2018, methodology): “We also evaluate our approach on the gait learning tasks for an underwater hexapod robot [2] to demonstrate the applicability of our approach for locomotion tasks on complex robot systems.”