Jul 29, 2024 · Both Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) have demonstrated powerful capabilities in policy search with different ...

EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search. Table 8. Details of the hyperparameters of EvoRainbow across tasks.

EvoRainbow integrates the latest advancements in ERL methods for policy search. Specifically, EvoRainbow contributes as follows:

ERL-Re$^2$ is a novel framework to integrate EA and RL. The cornerstone of ERL-Re$^2$ is two-scale representation: all EA and RL policies share the same ...

EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search Pengyi Li, Jianye HAO, Hongyao Tang, Xian Fu, Yan Zheng

EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search Pengyi Li, Jianye Hao, Hongyao Tang, Xian Fu, Yan Zheng ICML ...

EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search · pdf icon · Pengyi Li, YAN ZHENG, Hongyao Tang, Xian Fu, Jianye HAO.

EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search. P Li, Y Zheng, H Tang, X Fu, HAO Jianye. Forty-first ...

IEEE Transactions on Evolutionary Computation, 2024. 2, 2024. EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search. P Li ...

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