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Found 7 papers, 2 papers with code
Goal Exploration via Adaptive Skill Distribution for Goal-Conditioned Reinforcement Learning
Exploration efficiency poses a significant challenge in goal-conditioned reinforcement learning (GCRL) tasks, particularly those with long horizons and sparse rewards.
Bias Resilient Multi-Step Off-Policy Goal-Conditioned Reinforcement Learning
In goal-conditioned reinforcement learning (GCRL), sparse rewards present significant challenges, often obstructing efficient learning.
Goal Exploration Augmentation via Pre-trained Skills for Sparse-Reward Long-Horizon Goal-Conditioned Reinforcement Learning
In GCRL, exploring novel sub-goals is essential for the agent to ultimately find the pathway to the desired goal.
Multi-View Reinforcement Learning
This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models.
Learning Shared Dynamics with Meta-World Models
Humans have consciousness as the ability to perceive events and objects: a mental model of the world developed from the most impoverished of visual stimuli, enabling humans to make rapid decisions and take actions.
Learning to Communicate Implicitly By Actions
The auxiliary reward for communication is integrated into the learning of the policy module.
Unsupervised Deep Domain Adaptation for Pedestrian Detection
This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes.
