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Found 12 papers, 3 papers with code
SplAgger: Split Aggregation for Meta-Reinforcement Learning
However, it remains unclear whether task inference sequence models are beneficial even when task inference objectives are not.
Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control
Learning a universal policy across different robot morphologies can significantly improve learning efficiency and enable zero-shot generalization to unseen morphologies.
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
Recurrent Hypernetworks are Surprisingly Strong in Meta-RL
While many specialized meta-RL methods have been proposed, recent work suggests that end-to-end learning in conjunction with an off-the-shelf sequential model, such as a recurrent network, is a surprisingly strong baseline.
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System
Specifically, the first-order average consensus algorithm is utilized to expand the observations of the DESS state in a fully-decentralized way, and the initial actions (i. e., output power) are decided by the agents (i. e., energy storage units) according to these observations.
Single-View View Synthesis with Self-Rectified Pseudo-Stereo
In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction.
Universal Morphology Control via Contextual Modulation
Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control.
A Survey of Meta-Reinforcement Learning
Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible.
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting
To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem.
On the Practical Consistency of Meta-Reinforcement Learning Algorithms
We further find that theoretically inconsistent algorithms can be made consistent by continuing to update all agent components on the OOD tasks, and adapt as well or better than originally consistent ones.
Graph Policy Network for Transferable Active Learning on Graphs
Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields.
A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images
A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network.
