强化学习:人形机器人 —— soft-q-leanring的官方实现的配置环境,项目源码地址:https://github.com/rail-berkeley/softlearning调试这个代码其实没有什么实际意义,这里只是做了个尝试,纯
Q-learning定义了一个Q(s,a)函数,它指在状态s下采取动作a后所得到的累计奖励的期望值。我们结合图1和图2来说明Q-learning的局限性。先看图1左边的图,在机器人位于初始状态时,机器人到达蓝色X位置有上下两条路径,那么这时候的Q函数可以用图2左边的灰色曲线来表示。这条曲线是双波峰形态的。Q-learning算法(例...
可以看到,Q learning中max操作,改为了softmax操作,使得对应非最优Q值的动作也能有概率被选择,从而提升算法的exploration和generalization。原paper中有证明这样的soft policy improvement可以使得soft Q function的数值增加。 我们只需要改变DQN的policy evaluation和policy improvement的代码,就可以实现soft-DQN。改动后计算TD...
Reinforcement Learning with Deep Energy Based Policies 论文地址 "soft Q learning" 笔记 标准的强化学习策略 $$\begin{equation}\pi^ _{std} = \underset{\pi}{ar
visualizing Q-function during training. lr (`float`): Learning rate used for the function approximators. discount (`float`): Discount factor for Q-function updates. tau (`float`): Soft value function target update weight. target_update_interval ('int'): Frequency at which target network updat...
Soft Q-Learning is a deep reinforcement learning framework for training expressive, energy-based policies in continuous domains. This implementation is based on rllab. Full algorithm is detailed in our paper, Reinforcement Learning with Deep Energy-Based Policies, and videos can be found here. Instal...
Soft Q-learning can be run either locally or through Docker. Prerequisites You will need to haveDockerandDocker Composeinstalled unless you want to run the environment locally. Most of the models require aMuJoColicense. Docker Installation
通过Soft Q-Learning的概念，SAC将最大熵与Soft Q-function相结合，定义了Energy-Based Policy，实现了策略与最大熵目标之间的紧密联系。这一创新使得SAC在最大化熵的同时，能够收敛于最优策略。在Soft Actor-Critic算法的具体实现中，包括了神经网络化表示、更新公式设计以及自动调整温度参数的机制。算法...
Tasks Edit Atari Games Continuous Control Decision Making Imitation Learning MuJoCo Games Q-Learning Datasets Edit MuJoCo OpenAI Gym Arcade Learning Environment Omniverse Isaac Gym Results from the Paper Edit Ranked #1 on MuJoCo Games on Walker2d Get a GitHub badge Task...