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)。
对于PPO通过5个学习率参数,每组实验做两遍,总共10个实验,目前共有8个CPU,每个实验需要1个CPU,Tune可以把这些实验放入到队列中。若目前CPU已满,则等待,下图所示为8个CPU正在作业,剩余2个实验正在等待中 4 RLLib使用场景—RL算法 RLLib基于Tune和Ray实现强化学习算法,下图基于IMPALA框架,图中Trainer维护一个model,每...
Class/Type: PPOAgentMethod/Function: stop导入包: rayrllibagentsppo每个示例代码都附有代码来源和完整的源代码,希望对您的程序开发有帮助。示例1def testPPOSampleWaste(self): ray.init(num_cpus=4) # Check we at least collect the initial wave of samples ppo = PPOAgent( env="CartPole-v0", config...
# 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...
public static void allSort(int number) { if (number < 0) { return; } Stri...
linux://rllib:learning_tests_multi_agent_cartpole_ppo_multi_cpuis flaky. Recent failures: -https://buildkite.com/ray-project/postmerge/builds/6106#0191b537-37bd-46a0-b190-d2e232bd9c0f -https://buildkite.com/ray-project/postmerge/builds/6082#0191a40b-1f90-4ede-a295-2dfdc79655c4 -https...
Description InigoGastesi InigoGastesi added bugSomething that is supposed to be working; but isn't triageNeeds triage (eg: priority, bug/not-bug, and owning component) on Jul 5, 2024 anyscalesam added rllibRLlib related issues on Jul 9, 2024 ...
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名字到其obs的映射,不是所有的agent每一步都会返回obs > print(env.reset()) { "car_1": [...
"multiagent": { # 从策略id到元祖 (policy_cls, obs_space, # act_space, config)的映射. See rollout_worker.py for more info. "policies": {}, #从agent id到policy id "policy_mapping_fn": None, # 有选择的选择要训练的策略 "policies_to_train": None, ...
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境中一起行动,而在幕后,它们在 n 个并行的...