Model-based Policy Gradient基于策略的梯度模型.ppt,Efficient Policy Gradient Optimization/Learning of Feedback Controllers Chris Atkeson Punchlines Optimize and learn policies. Switch from “value iteration” to “policy iteration”. This is a big switch
1.1 Backpropagate into the policy 从RL的整个框架来看,agent接收到state,根据policy得到action,environment利用dynamic来state和action得到next state。很直观的,如果学习得到了dynamic model,那么就可以直接通过BP来计算得到dynamic关于policy的gradient,从而利用gradient descent进行policy更新: 从而新的model-based RL的算法...
Policy-Based Methods: 尝试直接用参数学习 policy 的近似，并且根据 policy gradient 去更新所学的 polic...
Actor-Critic Policy Gradient Introduction 上一节说的是value function approximation,使用的是函数拟合。这一节说的就是采用概率的方法来表示:这一节主要是讲model-free的方法。 RL有value-base,policy-based,以及把两者进行结合的actor-aritic的方法。 使用policy-based RL的好处在于:更容易智能...
【RL】Vanilla Policy Gradient(VPG) 拟合这个策略,我们定义一个神经网络policynet。网络的输入是sss,输出是一个n维向量,对它进行softmax之后,得到n个不同的概率(其和为1),分别对应于最佳动作是各个aaa的...},a_{2},r_{2}\right) (s0,a0,r0,s1,a1,r1,s2,a2,r2),则我们用策略 π w \pi_w πw走...
Key: model-based policy planning in action space and parameter space ExpEnv: mujoco Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis ...
theoretical results show that the model gradient error matters in the policy optimization phrase. Then we propose a two-model-based learning method to control the prediction error and the gradient error. We separate the different roles of these two models at the...
theoretical results show that the model gradient error matters in the policy optimization phrase. Then we propose a two-model-based learning method to control the prediction error and the gradient error. We separate the different roles of these two models at the model l...
Create an internal environment model for a model-based policy optimization (MBPO) agent. Create an environment for training other types of reinforcement learning agents. You can identify the state-transition network using experimental or simulated data. ...
Key: model-based policy planning in action space and parameter space ExpEnv: mujoco Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis ...