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...
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，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...
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments" - openai/multiagent-particle-envs
[0]] = 1.0 else: agent.action.c = action[0] action = action[1:] # make sure we used all elements of action assert len(action) == 0 # reset rendering assets def _reset_render(self): self.render_geoms = None self.render_geoms_xform = None # render environment def render(self, ...
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 `...
In a multi-agent game though, this combined reward is often the composite reward from the actions of other agentss and the environment. Similarly, you might want to be able to attribute the source of this reward for various learning reasons, or for debugging purposes to find out the origin...
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...