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...
SARSA、以及近年来广泛使用的策略梯度法（Policy Gradient Methods）和深度强化学习算法（如DQN, TRPO, PP...
Δp = -ε ∑x0w(x0)Vp 1st order gradient Δp = -(∑x0w(x0)Vpp)-1 ∑x0w(x0)Vp 2nd order Can we make model-based policy gradient more efficient? Analytic Gradients Deterministic policy: u = π(x,p) Policy Iteration (Bellman Equation): Vk-1(x,p) = L(x,π(x,p)) + V(...
policy.In this work,we investigate how model learning and policy learning can share the same objective of maximizing the expected return in the real environment.We find model learning towards this objective can result in a target of enhancing the similarity between the gradient on g...
【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...
模型的强化学习概要 之前学model-freeRL的时候 (1)从经验中利用 policy gradient 直接学习policy (2)利用 MC 或者 TD学习value function 本次课将会讲到model-basedRL【在讲 MDP 时有提到,有model时就可以进行策略迭代和值迭代】 (1)从经验中学习环境的model(这一点是跟我们之前 MDP 不同之处 ...
Key: multimodal policy learning, reparameterized policy gradient ExpEnv: Meta-World, mujoco Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy Xiyao Wang, Wichayaporn Wongkamjan, Ruonan Jia, Furong Huang Key: policy-adapted model learning, weight design ExpEnv: mujoco Predictable...
The model-specific trajectories are used to evaluate the policy loss (6) individually for each model to obtain a scalar value expressing the model’s quality (not for gradient descent). Comparing the ith model’s loss values of the current episode, i.g., Lpol,inew, with the loss of ...
On the model-based stochastic value gradient for continuous reinforcement learning Model-based reinforcement learning approaches add explicit domain knowledge to agents in hopes of improving the sample-efficiency in comparison to model-free agents. However, in practice model-based methods are unable to ...
Key: multimodal policy learning, reparameterized policy gradient ExpEnv: Meta-World, mujoco Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy Xiyao Wang, Wichayaporn Wongkamjan, Ruonan Jia, Furong Huang Key: policy-adapted model learning, weight design ExpEnv: mujoco Predictable...