## 2 Materials and Methods
### Reinforcement Learning in Continuous Environments
The success rate against the training rate in DQN highlights the iterative nature of reinforcement learning, where the agent gradually improves its policy through experience. While the initial training phases may show slow progress, consistent optimization of training parameters and environment design can significantly enhance both the speed and quality of learning. Understanding this relationship is crucial for developing more efficient and robust reinforcement learning applications, particularly in complex domains such as autonomous robotic systems. Figure 7: (a) DQN-based path planning. (b) DMP-based path planning. ## 4 Discussion

The advent of Industry 4.0 has driven the need for more intelligent, autonomous, and flexible robotic systems. Traditional methods of programming industrial robots, which often involve manual coding and teach pendant programming, are becoming increasingly inadequate due to their time-consuming nature and lack of adaptability. Deep reinforcement learning (DRL) offers a promising alternative by enabling robots to learn optimal behaviors through interactions with their environment. This discussion explores the integration of DRL into the teaching strategies for industrial robot arms, highlighting its potential advantages, challenges, and future directions [19]. Traditional teaching methods for industrial robots primarily involve two approaches: manual programming and teach pendant programming. The manual programming method requires writing explicit code for each task, specifying precise movements and operations. It is highly detailed and accurate but lacks flexibility and is not easily scalable to different tasks or environments. Teach pendant programming involves using a handheld device to guide the robot through a series of movements, which are then recorded and replayed. While more intuitive than manual programming, it remains time-consuming and is limited by the operator's skill and experience. Both methods require significant human intervention and expertise, making them less suitable for complex, dynamic, and unstructured environments. In a software setup where deep reinforcement learning (DRL) is connected to a robot model, several key components must be implemented to enable the robot to learn from and interact with its environment. A simulation platform (Simulink) models the robot and its environment. This environment simulates physical dynamics, sensor feedback, and constraints, allowing the DRL algorithm to train safely without the risk of damaging real hardware. The robot model in the simulation environment represents its kinematics, dynamics, sensors, and actuators. The simulated robot responds to control commands and updates its state based on simulated physics. The simulation continuously provides feedback on the robot's state (e.g., joint angles, velocities, end-effector position) and environment (e.g., distances to obstacles, positions of target objects). These state data are sent to the DRL agent as an observation vector or a set of images if a vision-based model is used. In a reinforcement learning setup, this information constitutes the state \(st\), which will be used as input for the agent's neural network to decide the next action. The DRL agent receives the current state \(st\) and, based on its learned policy, selects an action at. This action is represented as control signals, which could be joint torques, velocities, or positional changes in the robot model. The selected action is then converted to control commands that are sent to the robot model in the simulation environment, updating its state accordingly. Figure 8: Success rate against training rate. After executing an action, the environment calculates a reward \(rt\) based on task-specific criteria, such as the robot's proximity to a goal, success in grasping an object, or avoiding obstacles. This integration connects a DRL agent to a robot model by simulating real-world physics and feedback loops.