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...
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...
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...
Star561 master BranchesTags Code Folders and files Name Last commit message Last commit date Latest commit History 12 Commits common maddpg model/simple_tag README.md agent.py main.py runner.py MADDPG This is a pytorch implementation of MADDPG onMulti-Agent Particle Environment(MPE), the correspo...
We evaluate the proposed method on the competitive scenario in multiagent particle environment (MPE). Simulation results show that the agents are able to learn better policies with opponent portrait in competitive settings.doi:10.1002/int.22594Yuxi Ma...
MPE 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 ...
We evaluate our methods by four challenging tasks, three of which are based on the multi-agent particle environment (MPE) [29], and the other is a fully cooperative football game [30]. Experiments show that our algorithm can form an effective interactive network, leading to a higher reward ...
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.environment importMultiAgentEnv[as 别名]defmake_env(scenario_name, benchmark=False):''' ...
Ensure that multiagent-particle-envs has been added to your PYTHONPATH (e.g. in ~/.bashrc or ~/.bash_profile). To run the code, cd into the experiments directory and run train.py: python train.py --scenario simple You can replace simple with any environment in the MPE you'd like...
MPE: A set of simple nongraphical communication tasks, originally fromhttps://github.com/openai/multiagent-particle-envs SISL: 3 cooperative environments, originally fromhttps://github.com/sisl/MADRL Installation To install the base PettingZoo library:pip install pettingzoo. ...