Policy Optimization by Genetic Distillation

By Tanmay Gangwani, Jian Peng. 2017

46 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Bin Liang
2. Dietrich Paulus
3. Bo Yuan
4. Xueqian Wang
5. Tiantian Zhang
6. Viktor Seib
7. Raphael Memmesheimer
8. Ivanna Mykhalchyshyna

Citations in the corpus

• Simitate: A Hybrid Imitation Learning Benchmark (2019, positive): “Simitate will be applicable for approaches in different fields like imitation learning through reinforcement learning [18], genetic programming [19] or generative adversarial networks [20].”
• Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation (2021, background): “It is often used for policy transfer from one model to another (e.g., policy distillation [39], genetic policy optimization (GPO) [40], and distillation for continual reinforcement learning (DisCoRL) [41]).”