arXiv:1709.05070
공식 코드 · Papers with Code
Shapechanger: Environments for Transfer Learning
원문 초록
We present Shapechanger, a library for transfer reinforcement learning specifically designed for robotic tasks. We consider three types of knowledge transfer---from simulation to simulation, from simulation to real, and from real to real---and a wide range of tasks with continuous states and actions. Shapechanger is under active development and open-sourced at: https://github.com/seba-1511/shapechanger/.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Exploring the robustness of TractOracle methods in RL-based tractography
Deep Reinforcement Learning with Gradient Eligibility Traces
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling
토론
이 논문과 연결된 포럼 글이 아직 없습니다.