# 需要导入模块: from multiagent import environment [as 别名]# 或者: from multiagent.environment importMultiAgentEnv[as 别名]defmake_env(scenario_name, benchmark=False):# load scenario from scriptscenario = scenarios.load(scenario_name +".py").Scenario()# create worldworld = scenario.make_worl...
multiagent-particle-envs是OpenAI开源的多智能体学习环境。 一、安装 Link:https://github.com/openai/multiagent-particle-envs 简称小球环境,也是MADDPG用的环境,基本上可以看做是较为复杂的 gridworld 的环境。 在这个环境涵盖了ma里的竞争/协作/通讯场景,你可以根据你的需要设置agent的数量,选择他们要完成的任务...
4 changes: 2 additions & 2 deletions 4 grid2op/Environment/MultiAgentEnv.py Original file line numberDiff line numberDiff line change @@ -36,7 +36,7 @@ def __init__(self, env_name, agents_names, env_test : bool = False, agent_order = random_order agent_order_fn = random_order...
Multi-Agent Learning Env MADeepRL 铁FivePommerman(Simpler Env) Pommerman Official Websitewww.pommerman.com/ Chinese Guidence15 赞同 · 4 评论文章 SMAC Waiting for update ... 编辑于 2020-06-04 13:09 强化学习 (Reinforcement Learning) 赞同...
class MultiAgentEnv(gym.Env): metadata = { 'render.modes' : ['human', 'rgb_array'] } def __init__(self, world, reset_callback=None, reward_callback=None, observation_callback=None, info_callback=None, done_callback=None, shared_viewer=True): self.world = world self.agents = ...
如果使用参数'single-agent = True'实例化,则其行为类似于来自的常规 。 负责使用 API检索信息并在交通信号灯上。 该存储库的目标: 提供一个简单的界面,以与使用SUMO的交通信号控制强化学习一起使用 支持Multiagent RL 与gym.Env和流行的RL库(例如和 易于定制:状态和奖励定义易于修改 安装 安装最新版本的SUMO: ...
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, ...
Ubiquitous NetworkingCooperative PlatformMultiple DevicesMulti-AgentWith the development of embedded and communication technology, along with the rapid popularization of smart mobile devices in recent years, the communication techniques in ubiquitous networking......
class Agent(Entity): def __init__(self): super(Agent, self).__init__() # agents are movable by default self.movable = True # cannot send communication signals self.silent = False # 可以通信 # cannot observe the world self.blind = False ...
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, ...