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Found 3 papers, 1 papers with code
How does the structure embedded in learning policy affect learning quadruped locomotion?
This paper focuses on the latter because the structured policy is more intuitive and can inherit insights from previous model-based controllers.
Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)
A common way to solve this problem, known as Mixture-of-Experts, is to represent the policy as the weighted sum of multiple components, where different components perform well on different parts of the state space.
Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics
However, the local minima caused by unsuitable rewards and the overestimation of the cumulative reward impede the maximization of the cumulative reward.
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