To solve a series of problems, we propose a practical approach termed Federated Multi-Task Inverse Soft Actor-Critic (Fed-MT-ISAC), which extends the Inverse Soft Actor-Critic (ISAC) to federated and multi-task cases. We compare the performance of all algorithms in multi-style tasks and ...
(2024). Soft Actor-Critic Based Multi-drones Pursuit-Evasion Differential Game with Obstacles. In: Hua, Y., Liu, Y., Han, L. (eds) Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control. CCSICC 2023. Lecture Notes in Electrical Engineering, vol 1203. ...
optimization,多任务策略梯度算法 TRPO:multi-task trust region policy optimization,多任务在线策略梯度算法 SAC:multi-task soft actor-critic,多任务离线演员评论家算法 在线版本TE:task embeddings,多任务参数化策略参数,共享嵌入技能空间 RL2:在线元强化学习算法,在一个任务种训练一个具有隐状态的GRU网络,基础网络PPO...
Soft Actor-Critic. Contribute to uesta/Multi-Agent-Soft-Actor-Critic development by creating an account on GitHub.
在Actor-Critic原理一文中进行了策略梯度的推导,本文将Actor-Critic进一步扩展到Multi-Agent的设定下,内容主要参考论文Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments。 目录 Motivation 背景 方法 1. Motivation 多智能体协同/竞争的场景很常见,大约可以分成3大类:合作、竞争、竞合和自利。
由于要用到policy distillation,所以本文用的是随机策略(stochastic policy)。对于连续动作问题,作者将Soft Actor-Critic算法由单智能体扩展到多智能体。同时还要将actor的输出利用softmax函数转换为概率分布的形式。 Policy Distillation: 蒸馏策略的损失函数为
作者提出了一种actor-critic方法的变体MADDPG,对每个agent的强化学习都考虑其他agent的动作策略,进行中心化训练和非中心化执行,取得了显著效果。此外在此基础上,还提出了一种策略集成的训练方法,可以取得更稳健的效果(Additionally, we introduce a training regimen utilizing an ensemble of policies for each ...
multi-agent reinforcement learningsoft actor-criticvalue decompositioncredit assignmentIn recent years, significant progress has been made in the multi-target ... L Yue,R Yang,J Zuo,... - 《Drones》 被引量: 0发表: 2023年 Switching-aware multi-agent deep reinforcement learning for target intercep...
Soft Actor-Critic-Based DAG Tasks Offloading inMulti-access Edge Computing withInter-user Cooperation 来自 Springer 喜欢 0 阅读量: 53 作者:P Liu,S Ge,X Zhou,C Zhang,K Li 摘要: Multi-access edge computing (MEC) enables mobile applications, which consists of multiple dependent subtasks, to ...
4.1 Multi-Agent Actor Critic 该网络框架有如下假设条件: (1) the learned policies can only uselocal information (i.e. their own observations) at execution time, (2) we do not assume a differentiablemodel of the environment dynamics, unlike in [24], ...