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Found 6 papers, 2 papers with code
Code as Reward: Empowering Reinforcement Learning with VLMs
Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and provide feedback on task completion.
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications.
Policy Gradients Incorporating the Future
Reasoning about the future -- understanding how decisions in the present time affect outcomes in the future -- is one of the central challenges for reinforcement learning (RL), especially in highly-stochastic or partially observable environments.
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions
Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods.
Avoidance Learning Using Observational Reinforcement Learning
We define avoidance learning as the process of optimizing the agent's reward while avoiding dangerous behaviors given by a demonstrator.
Support vector comparison machines
In ranking problems, the goal is to learn a ranking function from labeled pairs of input points.
