A multi-agent cross-modal depth deterministic strategy gradient training method based on image input
A multi-agent, image input technology, applied in the field of reinforcement learning algorithms, can solve problems such as weak gradient guidance, reduced efficiency of actor network exploration, and impact of training actor exploration efficiency.
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[0056] Example 1:
[0057] Such as figure 1 As shown, a multi-agent cross-modal depth deterministic strategy gradient training method based on image input includes the following steps:
[0058] Step 1. Build an experiment platform in the simulator, define the types of interactive objects and robotic arms, define the ultimate goal and rewards and punishments of the robotic arm control task, and clarify the state space and action space of the dual agent;
[0059] The specific steps include:
[0060] S11. Use the open source simulation platform V-REP to build the experimental environment, the physics engine used is the Vortex open source physics engine, the robot arm type used is the UR5 robot, and the number of joints is 6;
[0061] S12. Set the task to be completed by the robotic arm control as the grasping task. The task is described as having multiple irregular objects of different sizes, shapes, and colors on the same height level of the robotic arm. The agent needs to control the ro...
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