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Found 6 papers, 1 papers with code
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning
To address this challenge, previous imitation learning methods exploit task-specific knowledge, e. g., labeling demonstrations manually or specifying termination conditions for each sub-task.
Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning
Our proposed method derives the variational lower bound of the situational mutual information to optimize it.
GIN: Graph-based Interaction-aware Constraint Policy Optimization for Autonomous Driving
Applying reinforcement learning to autonomous driving entails particular challenges, primarily due to dynamically changing traffic flows.
Learning Neural Processes on the Fly
In this paper, we propose a new algorithm called anytime neural processes that combines DNNs and SNNs and can work in both low-data and high-data regimes.
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning.
A Robotic Dating Coaching System Leveraging Online Communities Posts
Can a robot be a personal dating coach?
