首页|Faculty of Technology Researchers Discuss Research in Robotics (Comparative Analysis of Reinforcement Learning Algorithms for Bipedal Robot Locomotion)
Faculty of Technology Researchers Discuss Research in Robotics (Comparative Analysis of Reinforcement Learning Algorithms for Bipedal Robot Locomotion)
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Researchers detail new data in robotics. According to news reporting out of the Faculty of Technology by NewsRx editors, research stated, “In this research, an optimization methodology was introduced for improving bipedal robot locomotion controlled by reinforcement learning (RL) algorithms.” The news journalists obtained a quote from the research from Faculty of Technology: “Specifically, the study focused on optimizing the Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithms. The optimization process utilized the Tree-structured Parzen Estimator (TPE), a Bayesian optimization technique. All RL algorithms were applied to the same environment, which was created within the OpenAI GYM framework and known as the bipedal walker. The optimization involved the fine-tuning of key hyperparameters, including learning rate, discount factor, generalized advantage estimation, entropy coefficient, and Polyak update parameters. The study comprehensively analyzed the impact of these hyperparameters on the performance of RL algorithms. The results of the optimization efforts were promising, as the fine-tuned RL algorithms demonstrated significant improvements in performance.”
Faculty of TechnologyAlgorithmsEmerging TechnologiesMachine LearningReinforcement LearningRobotRobotics
NETL
NSTL
IEEE
