3. RL-based agent [RL-based、world model] Learning to Model the World with Language [RL-based、world model] MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning [RL-based、language knowledge、continual learning] Learning with Language Inference and Tips for Continual Reinforcement...
•据我们所知,这是第一次利用RL方法来决定视频和FEC速率,以提高视频会议的QoE。RL方法允许系统跟踪网络状况变化,并在丢包情况下优化视频和FEC比特率。 •我们在WebRTC框架之上实现R-FEC,并验证其在视频会议(包括现有的WebRTC)中优于最先进的基于rl的方法的性能增益。我们的研究结果表明,R-FEC在视频速率和视频...
Reinforcement learning (RL)-based semantic segmentation and attention based backpropagation convolutional neural network (ABB-CNN) for breast cancer identification and classification using mammogram imagesREINFORCEMENT learningCONVOLUTIONAL neural networks
OpenNetLab: Open Platform for RL-based Congestion Control for Real-Time Communications Jeongyoon Eo, Zhixiong Niu, Wenxue Cheng, Francis Y. Yan, Rui Gao, Jorina Kardhashi, Scott Inglis, Michael Revow, Byung-Gon Chun, Peng Cheng, Yongqiang Xiong ...
RL_based_syn the framework based on reinforcement learning for forward synthesis Code for the paper "Synthetically Feasible De Novo Molecular Design of Leads Based on a Reinforcement Learning Model: AI-Assisted Discovery of an Anti-IBD Lead Targeting CXCR4" Platform This research is based on MolPr...
In this paper, we propose a RL-based scheduling and placement method for DL jobs on large-scale GPU clusters. The key idea is to employ two RL agents with adaptable policy networks. These networks are capable of reducing computational complexity and supporting a flexible action space. First, ...
O1模型发布的第二天:RL-based CoT is all you need 在o1发布的第二天,我们了解到: o1-preview在ARC-AGI上得分21%(最高水平为46%):"总的来说,o1代表了从'记忆答案'到'记忆推理过程'的范式转变,但并未脱离通过拟合分布曲线来提高性能的更广泛范式,目的是使一切都处于分布内。" ...
However, we have not found RL-based autoscaling approaches for workflow applications comparing the performance of SARSA and Q-Learning during the learning process. This means analyzing the accumulated spent resources (time and monetary cost). We Concluding remarks Q-Learning and SARSA are two well-...
行为:动作分成两部分:换道(决策)和速度(运动)部分。作者说转向角用了rule-based方法,因为RL会经常S形开车(这个上面第2篇文章还特地对此加了一个奖励,难道不好用?)。 换道部分是三维A=(a_1,a_2,a_3),即左拐,保持,右拐。速度是个一维的加速度,网络范围是(-1,1),对应(-4.5,2.6)。
Decision Transformer、Trajectory Transformer 和序列推荐的区别