In this notebook we're going to be implementing reinforcement learning (RL) agents to play games against one another. Before reading this it is advised to be familiar with the TF-Agents and Deep Q-Learning; this tutorial will bring you up to speed. Introduction TF-Agents is a framework ...
Deep Q-learning (DQN) for Multi-agent Reinforcement Learning (RL) DQN implementation for two multi-agent environments: agents_landmarks and predators_prey (See details.pdf for a detailed description of these environments). Code structure ./environments/: folder where the two environments (agents_...
1.1 multi-agent RL 问题建模 1.2 multi-agent RL 求解范式 二、协作型的 multi-agent 系统 2.1 协作机制 2.2 对话系统 2.3 控制系统 三、竞争型的 multi-agent 系统 3.1 竞争型的解释及其与协作型的比较 3.2 典型的竞争型的案例 参考资料 在上一篇关于 RAG 的讨论中已经延伸出了 multi-agent 系统的概念,那...
gitclonehttps://github.com/hex-plex/KiloBot-MultiAgent-RLcdKiloBot-MultiAgent-RL pip install --upgrade absl-python \ tensorflow \ gym \ opencv-python \ tensorflow_probability \ keras \ pygame pip install -e gym-kiloBot This should fetch and install the basics packages needed and should install...
动作决策方面使用了双层动作执行,首先由RL-policy选定出一个Macro-action(宏观动作),然后用启发式算法根据Macro-Action生成若干Atom-action(微观/原子动作)。在网格探索任务中,Macro-action是一个具体的目标网格坐标,对于机器人来说,目标位置是一个宏观指令,它无法被机器人唯一确定的执行(即机器人可以选择多条路径到达...
Code and appendix can be found in https://github.com/ArronDZhang/FMRL_LA .Zhang, YiCommonwealth Scientific and Industrial Research Organisation (CSIRO)Wang, SenThe University of Queensland, St LuciaChen, ZhiThe University of Queensland, St LuciaXu, Xuwei...
Emergent Behaviors：这个更偏向另外一个视角，利用RL来做仿真，看看agent在不同环境下产生的行为。同时也...
【254】具有输入仿射系统动力学的连续时间非线性最优控制问题的强化学习(RL)算法 03:39 【255】【复现】带Pining控制的耦合神经网络的二部同步准则 01:53:34 【256】基于径向基(RBF)神经网络的单连杆和双连杆机械臂控制 01:14 【257】连续时间线性系统的积分Q学习与探索性策略迭代自适应最优控制 03:01 ...
【254】具有输入仿射系统动力学的连续时间非线性最优控制问题的强化学习(RL)算法 03:39 【255】【复现】带Pining控制的耦合神经网络的二部同步准则 01:53:34 【256】基于径向基(RBF)神经网络的单连杆和双连杆机械臂控制 01:14 【257】连续时间线性系统的积分Q学习与探索性策略迭代自适应最优控制 03:01 ...
import numpy as np from marlenv import RLEnv, DiscreteActionSpace, Observation N_AGENTS = 3 N_ACTIONS = 5 class CustomEnv(RLEnv[DiscreteActionSpace]): def __init__(self, width: int, height: int): super().__init__( action_space=DiscreteActionSpace(N_AGENTS, N_ACTIONS), observation_...