Search Results for author:
Found 24 papers, 9 papers with code
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks
The Space-Air-Ground Integrated Network (SAGIN) framework is a crucial foundation for future networks, where satellites and aerial nodes assist in computational task offloading.
RobustKV: Defending Large Language Models against Jailbreak Attacks via KV Eviction
Jailbreak attacks circumvent LLMs' built-in safeguards by concealing harmful queries within jailbreak prompts.
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
The Value Iteration Network (VIN) is an end-to-end differentiable architecture that performs value iteration on a latent MDP for planning in reinforcement learning (RL).
Highway Value Iteration Networks
To address this problem, we embed highway value iteration -- a recent algorithm designed to facilitate long-term credit assignment -- into the structure of VINs.
Highway Reinforcement Learning
We show, however, that such IS-free methods underestimate the optimal value function (VF), especially for large $n$, restricting their capacity to efficiently utilize information from distant future time steps.
Variational Delayed Policy Optimization
In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning (RL).
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
To address these challenges, we present a novel Auxiliary-Delayed Reinforcement Learning (AD-RL) method that leverages auxiliary tasks involving short delays to accelerate RL with long delays, without compromising performance in stochastic environments.
Deep Reinforcement Learning Based Placement for Integrated Access Backhauling in UAV-Assisted Wireless Networks
The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions.
Learning to Identify Critical States for Reinforcement Learning from Videos
Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic information about good policies can be extracted from offline data which lack explicit information about executed actions.
Mindstorms in Natural Language-Based Societies of Mind
What should be the social structure of an NLSOM?
Guiding Online Reinforcement Learning with Action-Free Offline Pretraining
In this paper, we investigate the potential of using action-free offline datasets to improve online reinforcement learning, name this problem Reinforcement Learning with Action-Free Offline Pretraining (AFP-RL).
Streaming, fast and accurate on-device Inverse Text Normalization for Automatic Speech Recognition
Automatic Speech Recognition (ASR) systems typically yield output in lexical form.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Research on Intellectual Property Resource Profile and Evolution Law
In the era of big data, intellectual property-oriented scientific and technological resources show the trend of large data scale, high information density and low value density, which brings severe challenges to the effective use of intellectual property resources, and the demand for mining hidden information in intellectual property is increasing.
An Intellectual Property Entity Recognition Method Based on Transformer and Technological Word Information
This paper proposes a method for extracting intellectual property entities based on Transformer and technical word information , and provides accurate word vector representation in combination with the BERT language method.
Resilient UAV Formation for Coverage and Connectivity of Spatially Dispersed Users
Unmanned aerial vehicles (UAVs) are a convenient choice for carrying mobile base stations to rapidly setup communication services for ground users.
DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training
A standard hardware bottleneck when training deep neural networks is GPU memory.
Mimic: An adaptive algorithm for multivariate time series classification
Time series data are valuable but are often inscrutable.
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
Greedy-Step Off-Policy Reinforcement Learning
Most of the policy evaluation algorithms are based on the theories of Bellman Expectation and Optimality Equation, which derive two popular approaches - Policy Iteration (PI) and Value Iteration (VI).
The Limit of the Batch Size
For the first time we scale the batch size on ImageNet to at least a magnitude larger than all previous work, and provide detailed studies on the performance of many state-of-the-art optimization schemes under this setting.
SMIX($λ$): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning
Learning a stable and generalizable centralized value function (CVF) is a crucial but challenging task in multi-agent reinforcement learning (MARL), as it has to deal with the issue that the joint action space increases exponentially with the number of agents in such scenarios.
Truly Proximal Policy Optimization
Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging tasks.
Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations
In real-world scenarios, the observation data for reinforcement learning with continuous control is commonly noisy and part of it may be dynamically missing over time, which violates the assumption of many current methods developed for this.
Trust Region-Guided Proximal Policy Optimization
We formally show that this method not only improves the exploration ability within the trust region but enjoys a better performance bound compared to the original PPO as well.
