DIAYN 学习笔记 - update discriminator/actor core algorithm for DIAYN Pseudocode for Actor Update: # Pseudocode for Actor Update (by ChatGPT lol # Initialize the actor's policy parameters initialize_actor_parameters() # Set learning rate learning_rate = 0.001 # Perform multiple iterations of actor ...
Pseudocode for our method is shown in Algorithm 1. Here, in order not to introduce the importance sampling, we use deep q-learning algorithm to train the critic network and the training samples for critic come from the starting experiences sampled in the process of episode-experience replay. Fo...
Algorithm 3: A3C Pseudocode 1: Set discount factor gamma 𝛾=0.99γ=0.99. 2: Set the global update interval 𝑡args_update_interval=5targs_update_interval=5. 3: Set the actor learning rate 𝛼actor=0.0005αactor=0.0005. 4: Set the critic learning rate 𝛼critic=0.001αcritic=0.001. ...
The pseudocode for the MAHGAC method is depicted in Algorithm 1. We train using soft actor–critic, an off-policy actor–critic method for maximum entropy reinforcement learning [31]. During training, at each time point, generate a set of rollout, consisting of a tuple (𝑜𝑡,𝑎𝑡,...
Pseudocode Documentation Documentation: PyTorch Version Saved Model Contents: PyTorch Version Documentation: Tensorflow Version Saved Model Contents: Tensorflow Version References Relevant Papers Other Public Implementations Background (Previously: Background for TD3) Soft Actor Critic (SAC) is an algorithm th...
The DESAC algorithm pseudocode is shown in Algorithm 1. The G-DESAC model diagram is shown in Figure 7. Algorithm 1: DESAC Input: Policy network 𝜋𝜃πθ, two Q networks 𝑄𝜙1Qϕ1, 𝑄𝜙2Qϕ2, target Q networks 𝑡𝑎𝑟𝑔𝑒𝑡−𝑄𝜙1target−Qϕ1, 𝑡...