2、cdmultiagent-particle-envs 3、pip install -e . 即可。 二、simple_world_comm环境详解 multiagent-particle-envs基于gym开发,所以环境创建流程基本于gym一致。multiagent-particle-envs包含9个环境,分别为simple、simple_adversary、simple_crypto、simple_push、simple_reference、simple_speaker_listener、simple_...
GitHub - openai/multiagent-particle-envs: Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"github.com/openai/multiagent-particle-envs p.s. MPE中粒子的运动既可以是离散的(前后左右四个方向),也可以是连续的(x...
MultiAgentEnv(gym.Env)类的env对象是强化学习算法与环境模拟器之间的桥梁，主要作用就是将由强化学习控制的智能体agents与环境模拟器中的agents连接起来，实现对环境模拟器的控制。因此，我们对于环境模拟器的控制最终都是通过env对象完成。env对象由env.world对象与env.agents对象组成，这里的world对象就是...
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments" - multiagent-particle-envs/multiagent/rendering.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" - multiagent-particle-envs/multiagent/environment.py at master · openai/multiagent-particle-envs
multiagent-particle-envs / README.mdStatus: Archive (code is provided as-is, no updates expected) 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 ...
多agent环境:https://github.com/openai/multiagent-particle-envs 1. 多agent环境中有效学习,前人一共提出2种方法,方法1单独的训练每个agent,其它agent作为环境的一部分,所以很难学习。方法2所有agent可以集体建模为单一agent,但是行动空间的大小相对于agent数量的指数增加,这种方法是不可扩展的。本文方法是用注意力...
make_env.py: contains code for importing a multiagent environment as an OpenAI Gym-like object. ./multiagent/environment.py: contains code for environment simulation (interaction physics, _step() function, etc.) ./multiagent/core.py: contains classes for various objects (Entities, Landmarks, ...
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 `...
env =MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation)returnenv 开发者ID:openai,项目名称:multiagent-particle-envs,代码行数:32,代码来源:make_env.py 示例3: make_env ▲点赞 5▼ # 需要导入模块: from multiagent import environment [as 别名]# 或者: from multiagent...