9/4/2023 0 Comments Webots controller![]() However, this poses additional challenges, due to the fact that simulated environments provide a varying degree of realism, so it is not always possible for the agent to observe and act exactly as it did during training in the real world. ![]() To circumvent these restrictions, researchers usually first run training sessions on realistic simulators, such as Gazebo , and OpenRAVE , where the simulation can run at accelerated speeds and with no danger, only later to transfer the trained agents on physical robots. Furthermore, during the initial stages of training, the agents take actions at random, potentially endangering the robot’s hardware. Despite the potential of DRL on robotics, such approaches usually require an enormous amount of time to sufficiently explore the environment and manage to solve the task, often suffering from low sample efficiency . In recent years, Deep Learning (DL) was further combined with RL to form the field of Deep Reinforcement Learning (DRL) , where powerful DL models were used to solve challenging RL problems.ĭRL is also increasingly used to train robots to perform complex and delicate tasks. The learning process is guided by a reward function, which typically expresses how close the agent is to reaching the desired target behavior. ![]() RL employs agents that learn by simultaneously exploring their environment and exploiting the already acquired knowledge to solve the task at hand. Reinforcement Learning (RL) is a domain of Machine Learning, and one of the three basic paradigms alongside supervised and unsupervised learning. The effectiveness of the proposed framework is demonstrated through code examples, as well as using three use cases of varying difficulty. Deepbots aims to enable researchers to easily develop DRL methods in Webots by handling all the low-level details and reducing the required development effort. To overcome these limitations, in this work we present an open-source framework that combines an established interface used by DRL researchers, the OpenAI Gym interface, with the state-of-the-art Webots robot simulator in order to provide a standardized way to employ DRL in various robotics scenarios. However, it is still not straightforward to employ such simulators in the typical DRL pipeline, since their steep learning curve and the enormous amount of development required to interface with DRL methods significantly restrict their use by researchers. Deep Reinforcement Learning (DRL) is increasingly used to train robots to perform complex and delicate tasks, while the development of realistic simulators contributes to the acceleration of research on DRL for robotics.
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