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> empirically demonstrate improved goal-reaching performance and robustness over current RL algorithms It's interesting that their choice of current algorithms
by klipt 6y ago
> empirically demonstrate improved goal-reaching performance and robustness over current RL algorithms
It's interesting that their choice of current algorithms includes PPO but not e.g. Deepmind's Rainbow agent that achieved state of the art performance on many measures: https://arxiv.org/abs/1710.02298 https://arxiv.org/abs/1710.02298
- tastroder 6y agoThey mention Rainbow in the related work section of the third paper listed there, Kumar, A., Peng, X. B., & Levine, S. (2019). Reward-Conditioned Policies. arXiv:1912.13465 as part of this remark: "they are also known to be notoriously challenging to use effectively, due to sensitivity to hyper parameters, high sample complexity, and a range of important and delicate implementation choices that have a large effect on performance [5, 6, 12, 15, 23, 24, 46]."