{"id":"2207.14561","title":"Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization","url":"https://codeofpaper.com/paper/2207.14561","arxiv_url":"https://arxiv.org/abs/2207.14561","published":"2022-07-29","code":"official","best":{"full_name":"yuki-kadokawa/cyclic-policy-distillation","url":"https://github.com/yuki-kadokawa/cyclic-policy-distillation","tier":"official","stars":3,"license":"MIT","framework":null,"language":"Python","last_commit":"2024-07-22T15:02:14"},"most_starred_community":null,"implementations":[{"full_name":"yuki-kadokawa/cyclic-policy-distillation","url":"https://github.com/yuki-kadokawa/cyclic-policy-distillation","tier":"official","stars":3,"license":"MIT","framework":null,"language":"Python","last_commit":"2024-07-22T15:02:14"}],"possible_matches":0}