这篇论文的核心发现是关于离线强化学习(Offline Reinforcement Learning, RL)的一个新方法:通过隐式值正则化(Implicit Value Regularization, IVR)来优化学习过程。作者提出了一个称为稀疏Q学习(Sparse Q-Learning, SQL)的新算法,这个算法在处理数据集时引入了稀疏性,可以更有效地学习值函数。这种方法在D4RL基准数据...
SQL与之间的CQL IQL OptiDICE均可以建立联系 ① CQL约束策略产生的Q函数而促进dataset中的Q函数;在SQL中(12)式中第一项若Q-V>0则会促进V函数,而第二项约束V函数,α起到了平衡作用;且SQL使用了与CQL相同的卡方-divergence来完成策略评估。相较于CQL,SQL只使用数据集以内的动作学习而CQL会使用策略产生的动作学...
Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents. In this paper we are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of ...
Deep Q learningURLLCSCMAReliabilityLatencyBit error probabilityThroughputCHANNEL ASSIGNMENTSparse code multiple access (SCMA) is a technology that allows for extremely low latency and high reliability in modern wireless communication networks. Moreover, due to the sparse layout of its codebooks, SCMA ...
extended Q-learningTendencies in Natural Language Processing (NLP) systems go towards the study of the semantic knowledge that can be extracted from the ... M Saiz-Noeda,M Palomar - Springer Berlin Heidelberg 被引量: 14发表: 2000年 Retention of balance coordination learning as influenced by ext...
【强化学习笔记】2020 李宏毅 强化学习课程笔记(PPO、Q-Learning、Actor + Critic、Sparse Reward、IRL),程序员大本营,技术文章内容聚合第一站。
The learning is regularised so that the learned representation and information-theoretic metric will (i) preserve the regularities of the visual/textual spaces, (ii) enhance structured sparsity, (iii) encourage small intra-concept distances, and (iv) keep inter-concept images separated. We ...
所提出的架构有四个不同的层,并解决了先前模型在输入维度的可扩展性方面的局限性。我们的SNN在经典强化学习和控制任务上进行评估,并与两种常见的RL算法进行比较:Q学习和深度Q网络(DQN)。实验表明,所提出的网络在具有六维观察空间的任务上优于Q学习,并且在稳定性和内存要求方面优于评估的DQN配置。
[transformers]" wget https://huggingface.co/neuralmagic/TinyLlama-1.1B-Chat-v0.4-pruned50-quant-ds/raw/main/recipe.yaml sparseml.transformers.text_generation.oneshot --model_name TinyLlama/TinyLlama-1.1B-Chat-v1.0 --dataset_name open_platypus --recipe recipe.yaml --output_dir ./obcq_...
Both ADAS-cog total 11, which is the 70 point total excluding Q4 (Delayed Word Recall) and Q14 (Number Cancellation), and ADAS-cog total 13, the 85 point total including Q4 and Q14, are significantly higher for MCI Converters than for MCI Non-Converters (p < 0.001); 4 ADAS-cog...