Integrating the Generative Adversarial Network for Decision Making in Reinforcement Learning for Industrial Robot Agents

Many robotics systems carrying certain payloads are employed in manufacturing industries for pick and place tasks. The system experiences inefficiency if more or less weight is introduced. If a different payload is introduced (either due to a change in the load or a change in the parameters of the robot system), the robot must be re-trained with the new weight/parameters and the new network must be trained. Parameters such as the robot weight, length of limbs, or new payload may vary for an agent depending on the circumstance. Parameter changes pose a problem to the agent in achieving the same goal it is expected to achieve with the original parameters. Hence, it becomes mandatory to re-train the agent with the new parameters in order for it to achieve its goal. This research proposes a novel framework for the adaption of varying conditions on a robot agent in a given simulated environment without any retraining. Utilizing the properties of Generative Adversarial Network (GAN), the agent is able to train only once with reinforcement learning and by tweaking the noise vector of the generator in the GAN network, the agent can adapt to new conditions accordingly and demonstrate similar performance as if it were trained with the new physical attributes using reinforcement learning. A simple CartPole environment is considered for the experimentation, and it is shown that with the propose approached the agent remains stable for more iterations. The approach can be extended to the real world in the future.