Обучение агентов в виртуальной среде KukaDiversObjectEnv

Abstract

The present study implements and compares the DQN, PPO, Parallel PPO, and Modified PPO algorithms in the PyBullet KukaDiverseObjectEnv environment. The algorithms are tested and evaluated in a simulated test mode to assess their performance. The experiments focus on metrics such as learning speed, stability, and task completion success rate. The results provide insights into the effectiveness of each algorithm in the tested environment, aiding in the optimization of reinforcement learning algorithms for complex environments and robotics