Modeling of Operational Control for Industrial Robots

Modeling of Operational Control for Industrial Robots

In reinforcement learning models, algorithms can be categorized into value-based and policy-based methods depending on whether the learning and updating processes follow the same policy.

• Value-based methods are effective for solving problems in low-dimensional spaces. These approaches, such as Q-learning and Deep Q-Networks (DQN), focus on estimating value functions (e.g., state-value or action-value functions) to derive optimal policies.

• Policy-based methods are better suited for high-dimensional and high-frequency action spaces. These methods, such as Policy Gradient algorithms, directly optimize the policy function, making them more capable of handling complex, continuous control tasks.

While policy-based methods excel in managing high-dimensional problems, their step-by-step updates often suffer from low learning efficiency. To address this limitation, this paper proposes the Actor-Critic (AC) algorithm, which combines the strengths of both approaches. The AC algorithm can effectively handle continuous, high-dimensional spaces while enabling rapid single-step learning.

In the Actor-Critic model:

• The Actor network uses policy gradients with the value function as a baseline for iteration. It interacts directly with the environment, observes the current state s, and selects actions based on s. The Actor then adjusts its policy based on evaluations from the Critic network to improve future rewards.

• The Critic network evaluates the value of actions and outputs the state-value function.

• The model starts with random initial states. In obstacle avoidance applications, the Actor network generates action policies and outputs robotic arm control commands, while the Critic network assesses action values.

• A reward function fine-tunes the parameters of both networks, ensuring more accurate Critic evaluations and enabling the Actor to generate more precise motion trajectories.

The working principle of the Actor-Critic algorithm is illustrated in Figure 1.

Application of Actor-Critic Algorithm in Robotic Arm Control

The intelligent workflow for applying deep reinforcement learning with the Actor-Critic algorithm to robotic arm operations is as follows:

1. Define the State Space
   Establish the state space for the robotic arm's operational task, including the arm's current position, joint angles, velocity, and the status/position of workpieces.

2. Define the Action Space
   Specify the action space, which consists of control commands such as target positions or joint angles for the robotic arm.

3. Build the Environment Model
   Develop a simulation environment that mimics the robotic arm's motion and operational processes, providing state observations and reward feedback.

4. Design the Reward Function
   Create a reward function based on task objectives (e.g., accuracy, efficiency) to evaluate the robotic arm's actions and encourage optimal policy learning.

5. Construct Neural Networks

• Actor Network: Generates control policies and outputs arm movement commands.

• Critic Network: Evaluates action values and outputs state-value functions.

6. Initialize Network Parameters
   Randomly initialize the parameters of both the Actor and Critic networks.

7. Collect Training Data
   Execute the robotic arm in the simulated environment to gather state-action-reward trajectories for training.

8. Train the Networks
   Optimize the Actor's policy and Critic's value function using dynamic programming and sampling techniques. Reinforcement learning optimization methods (e.g., Policy Gradient) are applied to update the network parameters iteratively.