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Found 17 papers, 10 papers with code
Learning a Universal Human Prior for Dexterous Manipulation from Human Preference
Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands.
Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection
When extracting object knowledge from PVLMs, the former adaptively transforms object proposals and adopts object-aware mask attention to obtain precise and complete knowledge of objects.
Ranked #6 on
Open Vocabulary Object Detection
on MSCOCO
PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding
To alleviate these drawbacks, we propose a one-stage end-to-end Pixel-Phrase Matching Network (PPMN), which directly matches each phrase to its corresponding pixels instead of region proposals and outputs panoptic segmentation by simple combination.
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games
This paper proposes new, end-to-end deep reinforcement learning algorithms for learning two-player zero-sum Markov games.
Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation
Referring video object segmentation aims to predict foreground labels for objects referred by natural language expressions in videos.
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game
A fully distributed MARL algorithm is proposed to approximate the Nash equilibrium of the game.
Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center
This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Reinforced Workload Distribution Fairness
Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services.
Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation
Though 3D convolutions are amenable to recognizing which actor is performing the queried actions, it also inevitably introduces misaligned spatial information from adjacent frames, which confuses features of the target frame and yields inaccurate segmentation.
Ranked #5 on
Referring Expression Segmentation
on J-HMDB
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning
However, grasping distinguishable skills for some tasks with non-unique optima can be essential for further improving its learning efficiency and performance, which may lead to a multimodal policy represented as a mixture-of-experts (MOE).
DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration
One of the main challenges of transferring the policy learned in a simulated environment to real world, is the discrepancy between the dynamics of the two environments.
Robotics
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.
Efficient Reinforcement Learning Development with RLzoo
RLzoo provides developers with (i) high-level yet flexible APIs for prototyping DRL agents, and further customising the agents for best performance, (ii) a model zoo where users can import a wide range of DRL agents and easily compare their performance, and (iii) an algorithm that can automatically construct DRL agents with custom components (which are critical to improve agent's performance in custom applications).
Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics
Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem.
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
That is, there is not yet a general evaluation platform for research on multi-agent intelligence.
TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation
Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions.
Deep Reinforcement Learning for Intelligent Transportation Systems
Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe.
