MPE(multiagent particle environment)是由OpenAI开发的一套时间离散、空间连续的二维多智能体环境,该环境通过控制二维空间中不同角色粒子(particle)的运动来完成一系列任务,使用方法与gym十分类似,目前被广泛用于各类MARL算法的仿真验证。 我的研究方向是多无人机协同控制,相关场景和MPE十分类似,因此我花了两天的时间研究...
Multi-Agent Particle EnvironmentA simple multi-agent particle world with a continuous observation and discrete action space, along with some basic simulated physics. Used in the paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments.Getting...
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments" - multiagent-particle-envs/multiagent/environment.py at master · openai/multiagent-particle-envs
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments" - openai/multiagent-particle-envs
Multi-Agent Particle Environment A simple multi-agent particle world with a continuous observation and discrete action space, along with some basic simulated physics. Used in the paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. Getting started: To install, cd into the roo...
Github:https://github.com/openai/multiagent-particle-envs 论文Blog:Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments - 穷酸秀才大艹包 - 博客园 (cnblogs.com) 创造新环境 您可以通过实现上面的前4个函数来创建新的场景 (`make_world()`, `reset_world()`, `reward()`, and `...
The Multi-Agent Particle Environments (MPE) were introduced as part of Mordatch and Abbeel [2017] and first released as part of Lowe et al. [2017]. These are 9 communication oriented environments where particle agents can (sometimes) move, communicate, see each other, push each other around...
In addition, many experiments are conducted in a multi-agent environment, which is a modification on the basis of Particle world environment. By comparing with some other distributed PG methods and changing the number of agents, we verify the performance of IS-DAPGM is more efficient than the ...
Multiagent Particle-World Environments (MPEs) Google Research Football (GRF) StarCraftII (SMAC) v2 1. Usage WARNING: by default all experiments assume a shared policy by all agents i.e. there is one neural network shared by all agents ...
Multiagent Particle-World Environments (MPEs) Google Research Football (GRF) 1. Usage WARNING: by default all experiments assume a shared policy by all agents i.e. there is one neural network shared by all agents All core code is located within the onpolicy folder. The algorithms/ subfolder...