To obtain the best evacuation path, we propose the efficient multi-agent deep deterministic policy gradient (E-MADDPG) algorithm for crowd-evacuation path planning. E-MADDPG algorithm combines learning curves to improve the fixed experience pool of MADDPG algorithm and uses high-priority experience ...
Deep Deterministic Policy Gradient (DDPG)docs.cleanrl.dev/rl-algorithms/ddpg/
Off-Policy Deterministic Policy Gradient 的表达式如下所示: 其中,\beta表示随机行为策略(stochastic behaviour policy)。 Deterministic Policy Gradient is an off-policy actor-critic algorithm that learns adeterministic target policyfrom anexploratory behaviour policy. The basic idea is to choose actions accordi...
Deep Deterministic Policy Gradient (DDPG) is an algorithm which concurrently learns a Q-function and a policy. It uses off-policy data and the Bellman equation to learn the Q-function, and uses the Q-function to learn the policy. This approach is closely connected to Q-learning, and is mo...
Deep Deterministic Policy Gradient (DDPG) 是“Deterministic”(确定性)的,因为它使用了一个确定性策略网络,而不是像传统的强化学习算法(例如,基于策略梯度的算法)那样使用随机策略网络。 具体来说,DDPG 使用的是一个确定性策略函数,通常表示为 𝜇(𝑠),它在给定状态 𝑠时输出一个具体的动作 𝑎,而不是一...
The deep deterministic policy gradient (DDPG) algorithm is an off-policy actor-critic method for environments with a continuous action-space. A DDPG agent learns a deterministic policy while also using a Q-value function critic to estimate the value of the optimal policy. It features a target ...
Google DeepMind 提出的一种使用Actor Critic结构, 但是输出的不是行为的概率, 而是具体的行为, 用于连续动作 (continuous action) 的预测.DDPG结合了之前获得成功的DQN结构, 提高了Actor Critic的稳定性和收敛性. 算法{#算法} DDPG的算法实际上就是一种Actor Critic, ...
在连续控制领域,比较经典的强化学习算法就是DDPG(Deep Deterministic Policy Gradient)。DDPG 的特点可以从它的名字当中拆解出来,拆解成 Deep、Deterministic 和Policy Gradient。 Deep 是因为用了神经网络; Deterministic 表示 DDPG 输出的是一个确定性的动作,可以用于连续动作的一个环境; Policy 强化学习入坑之路06 时...
Actor 网络输入状态 ,输出动作,注意的是,连续环境的动作一般都有一个范围,这个范围在环境中已经定以好,使用 action_bound = env.action_space.high 即可获取。 如果actor 输出的动作超出范围会导致程序异常,所以在网络末端使用 tanh 函数把输出映射到 [-1.0, 1.0]之间。然后使用...
The deep deterministic policy gradient (DDPG) algorithm is an off-policy actor-critic method for environments with a continuous action-space. A DDPG agent learns a deterministic policy while also using a Q-value function critic to estimate the value of the optimal policy. It features a target ...