本文属于多智能体强化学习(MARL)领域,我们知道多智能体强化学习与传统的强化学习之间的区别在于,它有多个智能体(即agent),每一个agent要最大化各自的 reward function,他们之间可能是合作关系(当他们共享同一个reward function时)或者竞争关系(当他们零和博弈时)。另一个值得讨论的点在于 agent之间是否存在信息的交换...
This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments. Dealing with multiple agents is inherently more c...
Communication is an effective mechanism for coordinating the behaviors of multiple agents, broadening their views of the environment, and to support their collaborations. In the field of multi-agent deep reinforcement learning (MADRL), agents can improve the overall learning performance and achieve thei...
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement learning as a Service (RaaS) for real-world resource optimization. It can be applied to many important industrial domains, such ascontainer inventory managementin logistics,bike repositioningin transportation,virtual machine...
KiloBot-MultiAgent-RL This is an experimentation to learn about Swarm Robotics with help of MultiAgent Reinforcement learning. We have used KiloBot as a platform as these are very simple in the actions space and have very high degree of symmetry. The Main inspiration of this project is this ...
DI-engine - DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.Speech...
directly address the optimization problem containing multiple decision-making agents that operate in a shared environment distinctive intricacies that arise within multi-agent dynamics during training, holding the potential to yield a wide range of valuable behaviors fostering adaptive social strategies facilit...
The role concept provides a useful tool to design and understand complex multi-agent systems, which allows agents with a similar role to share similar behaviors. However, existing role-based methods use prior domain knowledge and predefine role structures and behaviors. In contrast, multi-agent rein...
This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning. We focus primarily on literature from recent years that combines deep reinforcement learning methods with a multi-agent scenario. To survey the works that constitute the contemporary ...
总的来说,{\bf{solve}}^i返回第i个agent在某个平衡点的最优策略,而{\bf{eval}}^i计算的是在假定所有agent保持在同一个平衡点上的时候,第i个agent在这个平衡点中期望的长远奖励。 3.2.2 基于策略的方法 基于多智能体系统的组合性质,基于价值的方法存在维数诅咒问题(在4.1节有进一步解释)。这一特征使得基于...