REPPO is an on-policy RL method that combines pathwise policy gradients with relative entropy constraints to achieve stable training and high sample efficiency without replay buffers.
This makes the hyperparameters, to- gether with the algorithm description, and the source code, acomplete algorithm specificationin the sense of Jordan et al
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Relative Entropy Pathwise Policy Optimization
REPPO is an on-policy RL method that combines pathwise policy gradients with relative entropy constraints to achieve stable training and high sample efficiency without replay buffers.