"group2": ["agent4", "agent5"], }) make_multi_agent:将gym.Env 转换为MultiAgentEnv 用于将任何单代理环境转换为 MA 的便捷包装器。 允许您将简单(单代理)gym.Env类转换为MultiAgentEnv类。该函数只是将给定`gym.Env`类的 n 个实例堆叠到一个统一的MultiAgentEnv类中并返回该类,从而假装代理在同一环...
What happened + What you expected to happen I'm trying to create a multi-agent external environment and train using the new API stack, but this error appears when I run the reproduction script below. Would appreciate any guidance about w...
My goal is to learn a single policy that is deployed to multiple agents (i.e. all agents learn the same policy, but are able to communicate with each other through a shared neural network). RLlib's multi-agent interface works with the dict indicating an action for each individual agent....
RLlib中的多智能体模型如下:(1)定义可用策略个数,(2)定义从agent到policy的映射,如下图: 环境需要是MultiAgentEnv的子类,它每一步从多个agent返回obs和rew。 # Example: using a multi-agent env > env = MultiAgentTrafficEnv(num_cars=20, num_traffic_lights=5) # Observations 是一个字典,从agent名字到...
在较高的层次上,RLlib提供了一个 Trainer 类,它保存着与环境交互的策略。通过trainer的接口,可以对策略进行训练、设置断点或计算一个动作。在多智能体训练(multi-agent training)中,trainer同时管理多个策略的查询(根据输入计算输出)和优化(训练策略网络)。
strategy 示例：离线数据集配置 示例：input, input_config, actions_in_input_normalized, input_evaluation, postprocess_inputs, shuffle_buffer_size 示例：output 示例：output_compress_columns, output_max_file_size 示例：多智能体环境配置 示例：multiagent 示例：日志记录器配置 logger_config ...
RLLib——基于Ray的分布式和Tune的调参,实现抽象RL算法,可支持层次RL和Multi Agent学习等 1 Ray使用场景—多进程(通过ray.remote装饰器实现) 2 Ray使用场景—进程间通信 通过拿到远程函数的ID,可以在集群的任何地方,通过get(ID)获取该函数返回值 3 Tune使用场景—调参 ...
# Example: using a multi-agent env> env = MultiAgentTrafficEnv(num_cars=20, num_traffic_lights=5)# Observations are a dict mapping agent names to their obs. Not all# agents need to be present in the dict in each time step.> print(env.reset()) { "car_1": [[...]], "car_2...
SUMO-RL通过用于交通信号控制的提供了一个简单的界面来实例化强化学习环境。 主类继承了的 。 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...
In particular, this output shows that the minimum reward attained on average per episode is 1.0, which in turn means that the agent always reached the goal and collected the maximum reward (1.0). Saving, loading, and evaluating RLlib models ...