Reinforcement Learning with Foundation Priors for Robotic Manipulation
A new framework called Reinforcement Learning with Foundation Priors (RLFP) has been introduced by researchers to enhance sample efficiency and automate the design of reward functions in robotic manipulation tasks. Central to this approach is the Foundation-guided Actor-Critic (FAC) algorithm, which allows embodied agents to explore more effectively through automatic reward functions. RLFP tackles significant issues associated with implementing reinforcement learning in real-world applications, such as data demands and the need for manual reward design. This framework incorporates insights and feedback from policy, value, and success-reward foundation models, providing three key advantages: improved sample efficiency, streamlined reward engineering, and enhanced exploration. The research can be found on arXiv with the identifier 2310.02635.
Key facts
- RLFP framework uses foundation models to guide RL agents
- FAC algorithm enables efficient exploration with automatic rewards
- Addresses data intensity and manual reward design in real-world RL
- Integrates policy, value, and success-reward foundation models
- arXiv paper ID: 2310.02635
- Published as a replace-cross announcement
- Focuses on robotic manipulation tasks
- Claims three benefits: sample efficiency, minimal reward engineering, effective learning
Entities
Institutions
- arXiv