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Found 4 papers, 1 papers with code
Zero-Shot Reward Specification via Grounded Natural Language
Reward signals in reinforcement learning can be expensive signals in many tasks and often require access to direct state.
Weakly-Supervised Trajectory Segmentation for Learning Reusable Skills
Instead of direct manual supervision which is tedious and prone to bias, in this work, our goal is to extract reusable skills from a collection of human demonstrations collected directly for several end-tasks.
Zero-Shot Visual Imitation
In our framework, the role of the expert is only to communicate the goals (i. e., what to imitate) during inference.
Loss is its own Reward: Self-Supervision for Reinforcement Learning
Reinforcement learning optimizes policies for expected cumulative reward.
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