Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.
I tried to use the unoptimized ddpg algorithm and the basic dqn algorithm for training, but the training effect was not ideal. I would like to ask whether there is a problem with my code logic, or whether the parameter settings are unreasonable. My code is as follows, no other changes have been made, please help me, thank you。
the_ddpg.txtthe_dqn.txtvaliading.txt
I tried to use the unoptimized ddpg algorithm and the basic dqn algorithm for training, but the training effect was not ideal. I would like to ask whether there is a problem with my code logic, or whether the parameter settings are unreasonable. My code is as follows, no other changes have been made, please help me, thank you。 the_ddpg.txt the_dqn.txt valiading.txt