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...
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments" - openai/multiagent-particle-envs
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...
Multi-Agent Particle Environment(MPE) torch=1.1.0 Quick Start $ python main.py --scenario-name=simple_tag --evaluate-episodes=10 Directly run the main.py, then the algrithm will be tested on scenario 'simple_tag' for 10 episodes, using the pretrained model. ...
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...
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...
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 ...